Network_Pharmacology_Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bioservices import UniProt\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pubchempy as pcp\n",
    "import time\n",
    "import urllib.error\n",
    "import subprocess\n",
    "import requests\n",
    "import shutil\n",
    "import csv\n",
    "import re\n",
    "import xml.etree.ElementTree as ET\n",
    "import openpyxl\n",
    "import chardet\n",
    "import json\n",
    "import subprocess\n",
    "import sys\n",
    "import time\n",
    "from time import sleep\n",
    "import chardet\n",
    "from matplotlib_venn import venn2\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 DRUG-INGREDIENS"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 提取中药组分"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用herbiv工具提取中药组分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 添加路径到 sys.path\n",
    "sys.path.append(r'c:\\users\\cdzyy\\appdata\\roaming\\python\\python39\\site-packages')\n",
    "from herbiv import analysis\n",
    "\n",
    "\n",
    "\n",
    "tcm, tcm_chem_links, chem, chem_protein_links, proteins = analysis.from_tcm(['柴胡', '黄芩'])\n",
    "tcm\n",
    "chem.head()\n",
    "chem_protein_links.head()\n",
    "proteins.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 数据初处理"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 各数据库合并并转为csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_encoding(file_path): # 获取文本文件的编码格式\n",
    "    with open(file_path, 'rb') as f:\n",
    "        result = chardet.detect(f.read())\n",
    "    return result['encoding'] #\n",
    "\n",
    "def merge_files(input_folder, output_folder): # 合并文件夹中的所有文件\n",
    "    for subdir, _, _ in os.walk(input_folder):\n",
    "        if subdir == input_folder:\n",
    "            continue\n",
    "\n",
    "        folder_name = os.path.basename(subdir) # 获取子文件夹的名称\n",
    "        output_file = os.path.join(output_folder, f\"{folder_name}.csv\") # 合并后的文件名\n",
    "        \n",
    "        files = glob.glob(f\"{subdir}/*.xlsx\") + glob.glob(f\"{subdir}/*.txt\") # 获取子文件夹中的所有文件\n",
    "        combined_data = []\n",
    "\n",
    "        for file in files: # 读取文件\n",
    "            if file.endswith('.xlsx') and not os.path.basename(file).startswith('~$'): # 如果是Excel文件\n",
    "                data = pd.read_excel(file, dtype=str, engine='openpyxl')\n",
    "            elif file.endswith('.txt'): # 如果是文本文件\n",
    "                encoding = get_encoding(file)\n",
    "                data = pd.read_csv(file, sep=\"\\t\", dtype=str, encoding=encoding) # 如果文本文件使用了其他分隔符，请修改这里\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "            filename = os.path.splitext(os.path.basename(file))[0] # 获取文件名\n",
    "            data.insert(0, 'herb_name', filename)\n",
    "            combined_data.append(data)\n",
    "\n",
    "        merged_data = pd.concat(combined_data, ignore_index=True) # 合并数据\n",
    "        merged_data.to_csv(output_file, index=False, encoding='utf-8-sig') # 保存数据\n",
    "\n",
    "input_folder = '01.Drug_Ingredients/01.ingredients_rawdata'\n",
    "output_folder = '01.Drug_Ingredients/02.ingredients_preprocesseddata'\n",
    "os.makedirs(output_folder, exist_ok=True) # 创建输出文件夹\n",
    "merge_files(input_folder, output_folder)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 数据添加"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HERB数据库_补充OB&DL&pubchem&smiles等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 02.HERB.csv and saved to 01.Drug_Ingredients\\03.ingredients_augmenteddata\\HERB_augmented.csv\n"
     ]
    }
   ],
   "source": [
    "input_file = r\"01.Drug_Ingredients\\02.ingredients_preprocesseddata\\02.HERB.csv\"\n",
    "herb_info_file = r\"HERB_ingredient_info_v2.xlsx\"\n",
    "output_folder = r\"01.Drug_Ingredients\\03.ingredients_augmenteddata\"\n",
    "\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "def merge_csv_and_xlsx(input_file, herb_info_file, output_folder):\n",
    "    # 读取herb_ingredient_info文件\n",
    "    herb_ingredient_info = pd.read_excel(herb_info_file)\n",
    "    \n",
    "    # 读取输入CSV文件\n",
    "    df = pd.read_csv(input_file)\n",
    "\n",
    "    # 保留原有的 \"Ingredient id\" 列\n",
    "    df_ingredient_ids = df['Ingredient id']\n",
    "\n",
    "    # 仅保留在当前表格中出现的 \"Ingredient_id\"\n",
    "    herb_ingredient_info_filtered = herb_ingredient_info[herb_ingredient_info['Ingredient_id'].isin(df_ingredient_ids)]\n",
    "\n",
    "    # 按照 'Ingredient id' 和 'Ingredient_id' 列合并两个表格\n",
    "    merged_data = df.merge(herb_ingredient_info_filtered, left_on='Ingredient id', right_on='Ingredient_id', how='left')\n",
    "\n",
    "    # 删除重复的 'Ingredient_id' 列\n",
    "    merged_data.drop(columns=['Ingredient_id'], inplace=True)\n",
    "\n",
    "    # 将合并后的数据保存到输出文件夹中\n",
    "    output_file_name = os.path.join(output_folder, \"HERB_augmented.csv\")\n",
    "    merged_data.to_csv(output_file_name, index=False, encoding='utf-8-sig')\n",
    "    print(f\"Processed {os.path.basename(input_file)} and saved to {output_file_name}\")\n",
    "\n",
    "merge_csv_and_xlsx(input_file, herb_info_file, output_folder)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETCM数据库_补充PubchemCID和SMELIS \n",
    "\n",
    "分子全部经过AMDETlab2.0预测，但下载数据仅有QED，详细数据需单独详情页获得。故需根据\"ingredient_id\"AND\"molecular_formula\"检索获得PubchemCID和SMELIS，对于无法从pubchem获得smile的分子通过数据库2d分子结构图，手动获取smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检索 (-)-(13R,19S)-7β,11α,19-trihydroxy-N-rnethyl-13-(S-2-methylbutyryloxy)-2α-propionyloxyhetisanium hy-droxide 和 C30H46NO7+ 时出现错误: identifier/cid cannot be None\n",
      "检索 (-)-(13R,19S)-11α,19-dihydroxy-N-methyl-13-(S-2-methylbutyryloxy)-2α-propionyloxyhetisanium hy-droxide 和 C30H46NO6+ 时出现错误: identifier/cid cannot be None\n",
      "检索 Chasmanine 和 C25H41NO6 时出现错误: 'PUGREST.ServerBusy'\n",
      "检索 Beiwutinine 和 C23H37NO10 时出现错误: identifier/cid cannot be None\n",
      "检索 Deltamine 和 C25H39NO7 时出现错误: identifier/cid cannot be None\n",
      "检索 crassicauline A 和 C35H49NO11 时出现错误: identifier/cid cannot be None\n",
      "检索 Foresticine 和 C24H39NO6 时出现错误: identifier/cid cannot be None\n",
      "检索 Deoxyaconitine 和 C33H45NO10 时出现错误: identifier/cid cannot be None\n",
      "检索 yokonoside 和 C20H21NO12 时出现错误: identifier/cid cannot be None\n",
      "检索 (6S)-1-isopropyl-3-isopropylidene-6-methyl-6-vinylcyclohexene 和 C15H24 时出现错误: 'PUGREST.ServerBusy'\n",
      "检索 [10]-Gingerdione 和 C21H32O4 时出现错误: <urlopen error [Errno 2] No such file or directory>\n",
      "检索 1-(3,4-dimethoxyphenyl)-5-hydroxy-decan-3-one 和 C19H30O4 时出现错误: identifier/cid cannot be None\n",
      "检索 [8]-gingerdiol 和 C19H32O4 时出现错误: identifier/cid cannot be None\n",
      "检索 5-hydroxy-1-(4-hydroxy-3-methoxyphenyl)-7-(3,4-dihydroxyphenyl)heptan-3-one 和 C21H26O6 时出现错误: identifier/cid cannot be None\n",
      "检索 acetoxy-[10]-gingerol 和 C23H36O5 时出现错误: identifier/cid cannot be None\n",
      "检索 alpha-cis-bergamotene 和 C15H24 时出现错误: <urlopen error [Errno 2] No such file or directory>\n",
      "检索 Gingerglycolipid C 和 C33H56O14 时出现错误: <urlopen error [Errno 2] No such file or directory>\n",
      "检索 Isobrucine 和 C23H28N2O4 时出现错误: identifier/cid cannot be None\n",
      "检索 Phellatin 和 C26H30O12 时出现错误: <urlopen error [Errno 2] No such file or directory>\n",
      "检索 Zingiberone 和 C15H24O 时出现错误: identifier/cid cannot be None\n",
      "检索 (-) -α-terpineol-8-O-β-D-glucopyranoside 和 C16H28O6 时出现错误: identifier/cid cannot be None\n",
      "检索 ephedradine D 和 C29H38N4O4 时出现错误: identifier/cid cannot be None\n",
      "检索 ephedradine C 和 C30H40N4O4 时出现错误: identifier/cid cannot be None\n",
      "检索 ephedradine A 和 C28H36N4O3 时出现错误: identifier/cid cannot be None\n",
      "检索 18beta-glycyrrhetic acid 和 C31H50O4 时出现错误: identifier/cid cannot be None\n",
      "检索 2-propylpentanoate 和 C8H15O2- 时出现错误: 'PUGREST.ServerBusy'\n",
      "检索 2,6,10-trimethyl-dodecane 和 C6H8O4 时出现错误: identifier/cid cannot be None\n",
      "检索 3,3'-Dimethylquercetin 和 C17H16O7 时出现错误: identifier/cid cannot be None\n",
      "检索 5Q7ZVV76EI 和 C38H32O8 时出现错误: identifier/cid cannot be None\n",
      "检索 Cyclobutanol 和 C6H12O 时出现错误: identifier/cid cannot be None\n",
      "检索 Dipropyl disulfide 和 C6H14 时出现错误: identifier/cid cannot be None\n",
      "检索 Glepidotin A 和 C24H30O3 时出现错误: identifier/cid cannot be None\n",
      "检索 Glyasperin K 和 C22H24O5 时出现错误: identifier/cid cannot be None\n",
      "检索 Valproate sodium 和 C8H16NaO2+ 时出现错误: identifier/cid cannot be None\n",
      "检索 Uralsaponin A 和 C42H66O14 时出现错误: identifier/cid cannot be None\n",
      "Index 0: Pubchem_CID: None, SMILES: None\n",
      "Index 1: Pubchem_CID: None, SMILES: None\n",
      "Index 2: Pubchem_CID: None, SMILES: None\n",
      "Index 3: Pubchem_CID: None, SMILES: None\n",
      "Index 4: Pubchem_CID: None, SMILES: None\n",
      "Index 5: Pubchem_CID: None, SMILES: None\n",
      "Index 6: Pubchem_CID: None, SMILES: None\n",
      "Index 7: Pubchem_CID: None, SMILES: None\n",
      "Index 8: Pubchem_CID: None, SMILES: None\n",
      "Index 9: Pubchem_CID: None, SMILES: None\n",
      "Index 10: Pubchem_CID: None, SMILES: None\n",
      "Index 11: Pubchem_CID: None, SMILES: None\n",
      "Index 12: Pubchem_CID: None, SMILES: None\n",
      "Index 13: Pubchem_CID: None, SMILES: None\n",
      "Index 14: Pubchem_CID: None, SMILES: None\n",
      "Index 15: Pubchem_CID: None, SMILES: None\n",
      "Index 16: Pubchem_CID: None, SMILES: None\n",
      "Index 17: Pubchem_CID: None, SMILES: None\n",
      "Index 18: Pubchem_CID: None, SMILES: None\n",
      "Index 19: Pubchem_CID: None, SMILES: None\n",
      "Index 20: Pubchem_CID: None, SMILES: None\n",
      "Index 21: Pubchem_CID: None, SMILES: None\n",
      "Index 22: Pubchem_CID: None, SMILES: None\n",
      "Index 23: Pubchem_CID: None, SMILES: None\n",
      "Index 24: Pubchem_CID: None, SMILES: None\n",
      "Index 25: Pubchem_CID: None, SMILES: None\n",
      "Index 26: Pubchem_CID: 3133561, SMILES: CCN1CC2(CCC(C34C2CC(C31)C56C4CC(C(C5)C(=C)C6O)O)O)C\n",
      "Index 27: Pubchem_CID: 14312993, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(CC(C6CC4C5C6OC(=O)C)OC)O)OC)O)COC\n",
      "Index 28: Pubchem_CID: None, SMILES: None\n",
      "Index 29: Pubchem_CID: None, SMILES: None\n",
      "Index 30: Pubchem_CID: None, SMILES: None\n",
      "Index 31: Pubchem_CID: None, SMILES: None\n",
      "Index 32: Pubchem_CID: None, SMILES: None\n",
      "Index 33: Pubchem_CID: None, SMILES: None\n",
      "Index 34: Pubchem_CID: None, SMILES: None\n",
      "Index 35: Pubchem_CID: 102484827, SMILES: CCN1CC2(CCC(C34C2CC(C31)C5(CCC6CC4C5C6(CO)O)O)O)C\n",
      "Index 36: Pubchem_CID: 102484828, SMILES: CCN1CC2(CCC(C3(C1)C2CCC45C3C(C(CC4)C(C5OC(=O)C)(COC(=O)C6=CC=CC=C6)O)O)O)C\n",
      "Index 37: Pubchem_CID: None, SMILES: None\n",
      "Index 38: Pubchem_CID: 441705, SMILES: CCN1CC2(C(CC(C34C2C(C(C31)C5(C6C4(CC(C6OC(=O)C7=CC=CC=C7)(C(C5O)OC)O)O)OC(=O)C)OC)OC)O)COC\n",
      "Index 39: Pubchem_CID: None, SMILES: None\n",
      "Index 40: Pubchem_CID: None, SMILES: None\n",
      "Index 41: Pubchem_CID: 20055771, SMILES: CCN1CC2(C(CC(C34C2C(C(C31)C5(C6C4CC(C6OC(=O)C7=CC=CC=C7)(C(C5O)OC)O)O)OC)OC)O)COC\n",
      "Index 42: Pubchem_CID: 78358535, SMILES: CN1CC2(CCC(C34C2C(C(C31)C5(C6C4CC(C6OC(=O)C7=CC=CC=C7)(C(C5O)OC)O)O)OC)OC)COC\n",
      "Index 43: Pubchem_CID: 24832659, SMILES: CN1CC2(C(CC(C34C2C(C(C31)C5(C6C4CC(C6OC(=O)C7=CC=CC=C7)(C(C5O)OC)O)O)OC)OC)O)COC\n",
      "Index 44: Pubchem_CID: 165359516, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(CC(C6CC4C5C6OC(=O)C)OC)O)OC)O)COC\n",
      "Index 45: Pubchem_CID: 441742, SMILES: CCN1CC2(CCC(C34C2CC(C31)C5(CC(C6CC4C5C6O)OC)O)O)C\n",
      "Index 46: Pubchem_CID: None, SMILES: None\n",
      "Index 47: Pubchem_CID: 441720, SMILES: CCN1CC2(CCC(C34C2CC(C31)C5(CC(C6CC4C5C6OC(=O)C)OC)O)O)COC\n",
      "Index 48: Pubchem_CID: None, SMILES: None\n",
      "Index 49: Pubchem_CID: None, SMILES: None\n",
      "Index 50: Pubchem_CID: None, SMILES: None\n",
      "Index 51: Pubchem_CID: 101986490, SMILES: CCN1CC2(CCC(C34C2C(C5(C31)C6(CC(C7CC4C6C7O)OC)OCO5)OC)O)COC\n",
      "Index 52: Pubchem_CID: None, SMILES: None\n",
      "Index 53: Pubchem_CID: 139075130, SMILES: CC(=O)OC1C2C(C3C4C56C1C3(CC2=C)C(C(C5C(C(C(C6OC(=O)C)OC(=O)C7=CC=CC=C7)OC(=O)C)(C)C=O)N4C)O)OC(=O)C8=CC=CC=C8\n",
      "Index 54: Pubchem_CID: 74346932, SMILES: CC(=O)OC1C2C(C3C4C56C1C3(CC2=C)C(C(C5C(C(C(C6OC(=O)C)OC(=O)C7=CC=CC=C7)OC(=O)C)(C)C=O)N4C)O)OC(=O)C8=CC=CC=C8\n",
      "Index 55: Pubchem_CID: 185591, SMILES: CCN1CC2(CCC(C34C2C(C(C31)(C5(CC(C6CC4C5C6OC)OC)O)O)OC)OC)COC\n",
      "Index 56: Pubchem_CID: 45598211, SMILES: CCN1CC2(CCC(C34C2C(C(C31)(C5(CC(C6CC4C5C6OC)OC)O)O)OC)OC)COC(=O)C7=CC=CC=C7NC(=O)C(C)CC(=O)N\n",
      "Index 57: Pubchem_CID: 101341025, SMILES: CCN1CC2(CCC(C34C2C(C(C31)(C5(CC(C6CC4C5C6OC)OC)O)O)OC)OC)COC(=O)C7=CC=CC=C7NC(=O)CC(C)C(=O)N\n",
      "Index 58: Pubchem_CID: 441728, SMILES: CCN1CC2(CCC(C34C2C(C5(C31)C6(CC(C7CC4(C6C7OC)O)OC)OCO5)OC(=O)C)OC)C\n",
      "Index 59: Pubchem_CID: None, SMILES: None\n",
      "Index 60: Pubchem_CID: None, SMILES: None\n",
      "Index 61: Pubchem_CID: None, SMILES: None\n",
      "Index 62: Pubchem_CID: None, SMILES: None\n",
      "Index 63: Pubchem_CID: None, SMILES: None\n",
      "Index 64: Pubchem_CID: 5259652, SMILES: CC12CCCC34C1CC(C3N(C2)C)C56C4CC(CC5)C(C6O)(CO)O\n",
      "Index 65: Pubchem_CID: 72307, SMILES: C1C2C(COC2C3=CC4=C(C=C3)OCO4)C(O1)C5=CC6=C(C=C5)OCO6\n",
      "Index 66: Pubchem_CID: 20055981, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(CC(C6CC4C5C6OC(=O)C7=CC=C(C=C7)OC)OC)OC(=O)C)OC)OC)COC\n",
      "Index 67: Pubchem_CID: None, SMILES: None\n",
      "Index 68: Pubchem_CID: 14163819, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(C6C4CC(C6O)C(C5O)OC)O)OC)O)COC\n",
      "Index 69: Pubchem_CID: 102339571, SMILES: C(C(CO)OC1C(C(C(O1)C(CO)O)OC2C(C(C(O2)C(CO)O)O)O)O)O\n",
      "Index 70: Pubchem_CID: 72201752, SMILES: C[N+]1(CCC2=CC(=C(C3=C2C1CC4=C3C(=C(C=C4)O)O)OC)OC)C.[OH-]\n",
      "Index 71: Pubchem_CID: 76189740, SMILES: CN1CC2(CCC(C34C2C(C(C31)C5(C6C4CC(C6O)(C(C5O)OC)O)O)OC)OC)COC\n",
      "Index 72: Pubchem_CID: 441737, SMILES: CC(=O)OC12C3C(CC(C3OC(=O)C4=CC=CC=C4)(C(C1O)OC)O)C56C(CCC7(C5C(C2C6N(C7)C)OC)COC)OC\n",
      "Index 73: Pubchem_CID: 102146471, SMILES: CC(=O)OC12C3C(CC(C3OC(=O)C4=CC=CC=C4)C(C1O)OC)C56C(CCC7(C5C(C2C6N(C7)C)OC)COC)OC\n",
      "Index 74: Pubchem_CID: 170157, SMILES: CCN1CC2(CCC(C34C2CC(C31)C5(CC(C6CC4C5C6O)OC)O)O)C\n",
      "Index 75: Pubchem_CID: None, SMILES: None\n",
      "Index 76: Pubchem_CID: None, SMILES: None\n",
      "Index 77: Pubchem_CID: None, SMILES: None\n",
      "Index 78: Pubchem_CID: None, SMILES: None\n",
      "Index 79: Pubchem_CID: None, SMILES: None\n",
      "Index 80: Pubchem_CID: None, SMILES: None\n",
      "Index 81: Pubchem_CID: None, SMILES: None\n",
      "Index 82: Pubchem_CID: 76189547, SMILES: CN1CC2(C(CC(C34C2C(C(C31)C5(C6C4CC(C6O)(C(C5O)OC)O)O)OC)OC)O)COC\n",
      "Index 83: Pubchem_CID: 441747, SMILES: CC(=O)OC12C3C(CC(C3OC(=O)C4=CC=CC=C4)(C(C1O)OC)O)C56C(CC(C7(C5C(C2C6N(C7)C)OC)COC)O)OC\n",
      "Index 84: Pubchem_CID: 11568, SMILES: C1=CC(=CC(=C1)O)N\n",
      "Index 85: Pubchem_CID: None, SMILES: None\n",
      "Index 86: Pubchem_CID: None, SMILES: None\n",
      "Index 87: Pubchem_CID: 70690648, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(C6C4CC(C6OC(=O)C7=CC=CC=C7)(C(C5O)OC)O)OC)OC)OC)COC\n",
      "Index 88: Pubchem_CID: 120682, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(CC(C6CC4C5C6O)OC)O)OC)O)COC\n",
      "Index 89: Pubchem_CID: 5801, SMILES: C1=CC=C(C(=C1)N)O\n",
      "Index 90: Pubchem_CID: 6708531, SMILES: CC(=O)OC12C3C(CC(C3OC(=O)C4=CC=CC=C4)(C(C1O)OC)O)C56C(CC(C7(C5C(C2C6N(C7)C=O)OC)COC)O)OC\n",
      "Index 91: Pubchem_CID: 403, SMILES: C1=CC(=CC=C1N)O\n",
      "Index 92: Pubchem_CID: 91588, SMILES: CC1C2=CC(=C(C=C2CCN1)O)O\n",
      "Index 93: Pubchem_CID: 158048, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(CC(C6CC4C5C6O)OC)O)O)O)COC\n",
      "Index 94: Pubchem_CID: None, SMILES: None\n",
      "Index 95: Pubchem_CID: 14163819, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(C6C4CC(C6O)C(C5O)OC)O)OC)O)COC\n",
      "Index 96: Pubchem_CID: 14526618, SMILES: CCN1C2C3CC4C2(C5CCC4(C1O5)C)C6C37CC(C(=C)C7O)C(=O)C6\n",
      "Index 97: Pubchem_CID: 159891, SMILES: CCN1CC2(CCC(C34C2CC(C31)C5(CC(C6CC4C5C6O)OC)O)OC)COC\n",
      "Index 98: Pubchem_CID: 1174, SMILES: C1=CNC(=O)NC1=O\n",
      "Index 99: Pubchem_CID: 20055981, SMILES: CCN1CC2(CCC(C34C2C(C(C31)C5(CC(C6CC4C5C6OC(=O)C7=CC=C(C=C7)OC)OC)OC(=O)C)OC)OC)COC\n",
      "Index 100: Pubchem_CID: None, SMILES: None\n",
      "Index 101: Pubchem_CID: 15560276, SMILES: CC1CCC2C(C2(C)C)C3=C(CCC13)C\n",
      "Index 102: Pubchem_CID: 61123, SMILES: CC1(C2CCC(C2)(C1O)C)C\n",
      "Index 103: Pubchem_CID: 440966, SMILES: CC1(C2CCC(C2)C1=C)C\n",
      "Index 104: Pubchem_CID: None, SMILES: None\n",
      "Index 105: Pubchem_CID: 20831623, SMILES: CC1=CCCC(=C)C2CC(C2CC1)(C)C\n",
      "Index 106: Pubchem_CID: 443178, SMILES: CC1=CCC(CC1O)C(=C)C\n",
      "Index 107: Pubchem_CID: 178323, SMILES: CC1(C2C1C3C(CCC3(C)O)C(=C)CC2)C\n",
      "Index 108: Pubchem_CID: 12732529, SMILES: CC1CCC(C(C1)OC(=O)C)C(C)C\n",
      "Index 109: Pubchem_CID: None, SMILES: None\n",
      "Index 110: Pubchem_CID: None, SMILES: None\n",
      "Index 111: Pubchem_CID: None, SMILES: None\n",
      "Index 112: Pubchem_CID: None, SMILES: None\n",
      "Index 113: Pubchem_CID: None, SMILES: None\n",
      "Index 114: Pubchem_CID: None, SMILES: None\n",
      "Index 115: Pubchem_CID: None, SMILES: None\n",
      "Index 116: Pubchem_CID: None, SMILES: None\n",
      "Index 117: Pubchem_CID: 11051711, SMILES: CC(C)C12CCC(=C)C1C2\n",
      "Index 118: Pubchem_CID: None, SMILES: None\n",
      "Index 119: Pubchem_CID: None, SMILES: None\n",
      "Index 120: Pubchem_CID: 6992611, SMILES: CCCCCC(C)O\n",
      "Index 121: Pubchem_CID: None, SMILES: None\n",
      "Index 122: Pubchem_CID: 7139, SMILES: COC1=C(C=C(C=C1)CC(=O)O)OC\n",
      "Index 123: Pubchem_CID: None, SMILES: None\n",
      "Index 124: Pubchem_CID: None, SMILES: None\n",
      "Index 125: Pubchem_CID: None, SMILES: None\n",
      "Index 126: Pubchem_CID: None, SMILES: None\n",
      "Index 127: Pubchem_CID: None, SMILES: None\n",
      "Index 128: Pubchem_CID: 6999955, SMILES: CC(CCC=C(C)C)CC(=O)O\n",
      "Index 129: Pubchem_CID: None, SMILES: None\n",
      "Index 130: Pubchem_CID: None, SMILES: None\n",
      "Index 131: Pubchem_CID: None, SMILES: None\n",
      "Index 132: Pubchem_CID: None, SMILES: None\n",
      "Index 133: Pubchem_CID: None, SMILES: None\n",
      "Index 134: Pubchem_CID: None, SMILES: None\n",
      "Index 135: Pubchem_CID: 23786427, SMILES: CCCCCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 136: Pubchem_CID: None, SMILES: None\n",
      "Index 137: Pubchem_CID: None, SMILES: None\n",
      "Index 138: Pubchem_CID: None, SMILES: None\n",
      "Index 139: Pubchem_CID: None, SMILES: None\n",
      "Index 140: Pubchem_CID: None, SMILES: None\n",
      "Index 141: Pubchem_CID: 6442560, SMILES: CCCCCCCC=CC(=O)CCC1=CC(=C(C=C1)O)OC\n",
      "Index 142: Pubchem_CID: None, SMILES: None\n",
      "Index 143: Pubchem_CID: 12302242, SMILES: CC1CCC(C2=C1C=CC(=C2)C)C(C)C\n",
      "Index 144: Pubchem_CID: None, SMILES: None\n",
      "Index 145: Pubchem_CID: 5353086, SMILES: CC(=CCCC(=CCC=C(C)C=C)C)C\n",
      "Index 146: Pubchem_CID: 443162, SMILES: CC1=CCC(CC1)C(C)(C)O\n",
      "Index 147: Pubchem_CID: 442482, SMILES: CC1=CCC(C=C1)C(C)C\n",
      "Index 148: Pubchem_CID: None, SMILES: None\n",
      "Index 149: Pubchem_CID: 16724, SMILES: CC1=CCC(CC1=O)C(=C)C\n",
      "Index 150: Pubchem_CID: 2724159, SMILES: CC(=C)C1CCC(=CC1)C=O\n",
      "Index 151: Pubchem_CID: None, SMILES: None\n",
      "Index 152: Pubchem_CID: 643779, SMILES: CC(=CCCC(=CC=O)C)C\n",
      "Index 153: Pubchem_CID: 1549108, SMILES: CC(=CCCC(=CCCC(=CCO)C)C)C\n",
      "Index 154: Pubchem_CID: 5280934, SMILES: CCC=CCC=CCC=CCCCCCCCC(=O)O\n",
      "Index 155: Pubchem_CID: 6430755, SMILES: CC1=CC2C(C2(C)C)CC1\n",
      "Index 156: Pubchem_CID: 5352437, SMILES: CC1=CCC(=C(C)CCC=C(C)C)CC1\n",
      "Index 157: Pubchem_CID: None, SMILES: None\n",
      "Index 158: Pubchem_CID: None, SMILES: None\n",
      "Index 159: Pubchem_CID: 6999975, SMILES: CC(CCC=C(C)C)CCOC(=O)C\n",
      "Index 160: Pubchem_CID: None, SMILES: None\n",
      "Index 161: Pubchem_CID: None, SMILES: None\n",
      "Index 162: Pubchem_CID: None, SMILES: None\n",
      "Index 163: Pubchem_CID: None, SMILES: None\n",
      "Index 164: Pubchem_CID: None, SMILES: None\n",
      "Index 165: Pubchem_CID: None, SMILES: None\n",
      "Index 166: Pubchem_CID: None, SMILES: None\n",
      "Index 167: Pubchem_CID: 86251913, SMILES: CCCCCCCCCCCC(=O)CC(=O)CCC1=CC(=C(C=C1)O)OC\n",
      "Index 168: Pubchem_CID: 5317599, SMILES: CCCCCCCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 169: Pubchem_CID: 5318276, SMILES: CCCC(CC(CCC1=CC(=C(C=C1)O)OC)O)O\n",
      "Index 170: Pubchem_CID: None, SMILES: None\n",
      "Index 171: Pubchem_CID: 11482504, SMILES: CCCC(=O)CC(CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 172: Pubchem_CID: 15839352, SMILES: CCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 173: Pubchem_CID: None, SMILES: None\n",
      "Index 174: Pubchem_CID: 5275727, SMILES: CCCCCC(CC(CCC1=CC(=C(C=C1)O)OC)O)O\n",
      "Index 175: Pubchem_CID: None, SMILES: None\n",
      "Index 176: Pubchem_CID: None, SMILES: None\n",
      "Index 177: Pubchem_CID: None, SMILES: None\n",
      "Index 178: Pubchem_CID: 11694761, SMILES: CCCCCC(=O)C=CCCC1=CC(=C(C=C1)O)OC\n",
      "Index 179: Pubchem_CID: 101941698, SMILES: CCCCCCCC(CC(CCC1=CC(=C(C=C1)O)OC)O)O\n",
      "Index 180: Pubchem_CID: None, SMILES: None\n",
      "Index 181: Pubchem_CID: 213821, SMILES: CCCCCCCCCC(=O)CCC1=CC(=C(C=C1)O)OC\n",
      "Index 182: Pubchem_CID: None, SMILES: None\n",
      "Index 183: Pubchem_CID: None, SMILES: None\n",
      "Index 184: Pubchem_CID: None, SMILES: None\n",
      "Index 185: Pubchem_CID: None, SMILES: None\n",
      "Index 186: Pubchem_CID: None, SMILES: None\n",
      "Index 187: Pubchem_CID: None, SMILES: None\n",
      "Index 188: Pubchem_CID: 94378, SMILES: CCCCCCCC(=O)CCC1=CC(=C(C=C1)O)OC\n",
      "Index 189: Pubchem_CID: 14524462, SMILES: CC(C(C1=CC(=C(C=C1)O)OC)O)O\n",
      "Index 190: Pubchem_CID: None, SMILES: None\n",
      "Index 191: Pubchem_CID: 519298, SMILES: CC1CCC(C2C1=CCC(=C2)C)C(C)C\n",
      "Index 192: Pubchem_CID: 10429233, SMILES: COC1=C(C=CC(=C1)CCC(=O)CC(=O)C=CC2=CC(=C(C=C2)O)OC)O\n",
      "Index 193: Pubchem_CID: 79022, SMILES: CC1(C2CC3C1C3(C2)C)C\n",
      "Index 194: Pubchem_CID: None, SMILES: None\n",
      "Index 195: Pubchem_CID: None, SMILES: None\n",
      "Index 196: Pubchem_CID: None, SMILES: None\n",
      "Index 197: Pubchem_CID: 73803060, SMILES: COC1=CC(=CC(=C1O)OC)C2CC(CC(O2)CCC3=CC(=C(C=C3)O)OC)O\n",
      "Index 198: Pubchem_CID: None, SMILES: None\n",
      "Index 199: Pubchem_CID: None, SMILES: None\n",
      "Index 200: Pubchem_CID: None, SMILES: None\n",
      "Index 201: Pubchem_CID: None, SMILES: None\n",
      "Index 202: Pubchem_CID: None, SMILES: None\n",
      "Index 203: Pubchem_CID: None, SMILES: None\n",
      "Index 204: Pubchem_CID: None, SMILES: None\n",
      "Index 205: Pubchem_CID: None, SMILES: None\n",
      "Index 206: Pubchem_CID: None, SMILES: None\n",
      "Index 207: Pubchem_CID: 44610342, SMILES: CCCCCCCC(=O)CC(=O)C=CC1=CC(=C(C=C1)O)OC\n",
      "Index 208: Pubchem_CID: 14999388, SMILES: CCCCCCCCCC(=O)CC(=O)C=CC1=CC(=C(C=C1)O)OC\n",
      "Index 209: Pubchem_CID: None, SMILES: None\n",
      "Index 210: Pubchem_CID: None, SMILES: None\n",
      "Index 211: Pubchem_CID: None, SMILES: None\n",
      "Index 212: Pubchem_CID: 5283469, SMILES: CCCCCC=CCC=CCCCCCCCC(=O)OCC(CO)O\n",
      "Index 213: Pubchem_CID: None, SMILES: None\n",
      "Index 214: Pubchem_CID: 586437, SMILES: CCCCC1=CC=C(O1)CCCC\n",
      "Index 215: Pubchem_CID: None, SMILES: None\n",
      "Index 216: Pubchem_CID: 137614, SMILES: CC=C(C)CCC=C(C)C\n",
      "Index 217: Pubchem_CID: None, SMILES: None\n",
      "Index 218: Pubchem_CID: None, SMILES: None\n",
      "Index 219: Pubchem_CID: None, SMILES: None\n",
      "Index 220: Pubchem_CID: 222284, SMILES: CCC(CCC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C\n",
      "Index 221: Pubchem_CID: 536562, SMILES: CC1CCC2C(C1O)C2(C)C\n",
      "Index 222: Pubchem_CID: 12741, SMILES: CCCCCCCCC(=O)C\n",
      "Index 223: Pubchem_CID: None, SMILES: None\n",
      "Index 224: Pubchem_CID: 17586, SMILES: CCC(C)OCC\n",
      "Index 225: Pubchem_CID: 5354264, SMILES: CCCC=C(CC)C=O\n",
      "Index 226: Pubchem_CID: None, SMILES: None\n",
      "Index 227: Pubchem_CID: 10976, SMILES: CCCCCC(C)O\n",
      "Index 228: Pubchem_CID: 7996, SMILES: CC(C)OCCO\n",
      "Index 229: Pubchem_CID: 5319500, SMILES: CC1=CC(=O)C2=C(C=C(C=C2O1)O)CC(=O)C\n",
      "Index 230: Pubchem_CID: 12330, SMILES: CCCCC=C(C)C\n",
      "Index 231: Pubchem_CID: 140334, SMILES: CCCCCCCCCCCC(=C)C\n",
      "Index 232: Pubchem_CID: 13187, SMILES: CCCCCCCC(=O)C\n",
      "Index 233: Pubchem_CID: 11622, SMILES: CCCCCCCCCCCC(=O)C\n",
      "Index 234: Pubchem_CID: 10394, SMILES: C1=CC(=CC=C1CCC(=O)O)O\n",
      "Index 235: Pubchem_CID: 5316611, SMILES: CC(=O)OC(CCC1=CC(=C(C=C1)O)OC)CC(CCC2=CC(=C(C(=C2)OC)O)OC)OC(=O)C\n",
      "Index 236: Pubchem_CID: None, SMILES: None\n",
      "Index 237: Pubchem_CID: None, SMILES: None\n",
      "Index 238: Pubchem_CID: None, SMILES: None\n",
      "Index 239: Pubchem_CID: None, SMILES: None\n",
      "Index 240: Pubchem_CID: None, SMILES: None\n",
      "Index 241: Pubchem_CID: 142315, SMILES: CC1(C2C1CC(CC2)(C)O)C\n",
      "Index 242: Pubchem_CID: 15512, SMILES: C1CC(CC=C1)CO\n",
      "Index 243: Pubchem_CID: 556685, SMILES: C1CC(CC=C1)O\n",
      "Index 244: Pubchem_CID: None, SMILES: None\n",
      "Index 245: Pubchem_CID: 550361, SMILES: CC(CCC=C(C)C)C1CCC(=O)C=C1\n",
      "Index 246: Pubchem_CID: 92516, SMILES: COCCC1=CC=C(C=C1)O\n",
      "Index 247: Pubchem_CID: 5315645, SMILES: CC(=CCCC1(C2CCC1(C(C2)O)C)C)C\n",
      "Index 248: Pubchem_CID: None, SMILES: None\n",
      "Index 249: Pubchem_CID: None, SMILES: None\n",
      "Index 250: Pubchem_CID: None, SMILES: None\n",
      "Index 251: Pubchem_CID: None, SMILES: None\n",
      "Index 252: Pubchem_CID: 22298189, SMILES: CCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)S(=O)(=O)O\n",
      "Index 253: Pubchem_CID: None, SMILES: None\n",
      "Index 254: Pubchem_CID: None, SMILES: None\n",
      "Index 255: Pubchem_CID: 35960, SMILES: COC1=CC(=CC(=C1O)OC)C=C\n",
      "Index 256: Pubchem_CID: None, SMILES: None\n",
      "Index 257: Pubchem_CID: 6428535, SMILES: CC1=CC2C(CC1)C(=CCCC2(C)C)C\n",
      "Index 258: Pubchem_CID: 12367, SMILES: CCCCCCCC(C)O\n",
      "Index 259: Pubchem_CID: None, SMILES: None\n",
      "Index 260: Pubchem_CID: None, SMILES: None\n",
      "Index 261: Pubchem_CID: 131637007, SMILES: CCCCCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)OCC\n",
      "Index 262: Pubchem_CID: None, SMILES: None\n",
      "Index 263: Pubchem_CID: 41170, SMILES: CC(CCC=C)O\n",
      "Index 264: Pubchem_CID: None, SMILES: None\n",
      "Index 265: Pubchem_CID: None, SMILES: None\n",
      "Index 266: Pubchem_CID: 21577374, SMILES: COC1=CC(=CC(=C1O)O)CCC(CC(=O)CCC2=CC(=C(C=C2)O)OC)O\n",
      "Index 267: Pubchem_CID: None, SMILES: None\n",
      "Index 268: Pubchem_CID: None, SMILES: None\n",
      "Index 269: Pubchem_CID: None, SMILES: None\n",
      "Index 270: Pubchem_CID: 3473, SMILES: CCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 271: Pubchem_CID: None, SMILES: None\n",
      "Index 272: Pubchem_CID: 17868, SMILES: CC1=CCC2(C1C2)C(C)C\n",
      "Index 273: Pubchem_CID: None, SMILES: None\n",
      "Index 274: Pubchem_CID: None, SMILES: None\n",
      "Index 275: Pubchem_CID: None, SMILES: None\n",
      "Index 276: Pubchem_CID: None, SMILES: None\n",
      "Index 277: Pubchem_CID: None, SMILES: None\n",
      "Index 278: Pubchem_CID: None, SMILES: None\n",
      "Index 279: Pubchem_CID: 5317587, SMILES: CCCCCC(CC(CCC1=CC(=C(C=C1)O)OC)OC(=O)C)OC(=O)C\n",
      "Index 280: Pubchem_CID: None, SMILES: None\n",
      "Index 281: Pubchem_CID: None, SMILES: None\n",
      "Index 282: Pubchem_CID: 11369949, SMILES: CCCCCC(CC(CCC1=CC(=C(C=C1)O)OC)O)O\n",
      "Index 283: Pubchem_CID: None, SMILES: None\n",
      "Index 284: Pubchem_CID: 14330776, SMILES: CCCCCC(CC(=O)CCC1=CC(=C(C=C1)OC(=O)C)OC)OC(=O)C\n",
      "Index 285: Pubchem_CID: None, SMILES: None\n",
      "Index 286: Pubchem_CID: None, SMILES: None\n",
      "Index 287: Pubchem_CID: 126890, SMILES: CCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)S(=O)(=O)O\n",
      "Index 288: Pubchem_CID: None, SMILES: None\n",
      "Index 289: Pubchem_CID: None, SMILES: None\n",
      "Index 290: Pubchem_CID: None, SMILES: None\n",
      "Index 291: Pubchem_CID: None, SMILES: None\n",
      "Index 292: Pubchem_CID: None, SMILES: None\n",
      "Index 293: Pubchem_CID: 5319663, SMILES: CCCCCC(CC(CCC1=CC(=C(C=C1)OC)OC)O)O\n",
      "Index 294: Pubchem_CID: 5281794, SMILES: CCCCCC=CC(=O)CCC1=CC(=C(C=C1)O)OC\n",
      "Index 295: Pubchem_CID: 11472344, SMILES: CCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 296: Pubchem_CID: None, SMILES: None\n",
      "Index 297: Pubchem_CID: None, SMILES: None\n",
      "Index 298: Pubchem_CID: 5317564, SMILES: CC(=CCCC(=CCOC1=C2C(=CC3=C1OC=C3)C=CC(=O)O2)C)C\n",
      "Index 299: Pubchem_CID: None, SMILES: None\n",
      "Index 300: Pubchem_CID: 5363397, SMILES: CC(=CCO)CCC=C(C)CO\n",
      "Index 301: Pubchem_CID: None, SMILES: None\n",
      "Index 302: Pubchem_CID: None, SMILES: None\n",
      "Index 303: Pubchem_CID: 102510965, SMILES: CCCCCCCCC(CC(=O)CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 304: Pubchem_CID: None, SMILES: None\n",
      "Index 305: Pubchem_CID: None, SMILES: None\n",
      "Index 306: Pubchem_CID: None, SMILES: None\n",
      "Index 307: Pubchem_CID: 65575, SMILES: CC1CCC2C13CCC(C(C3)C2(C)C)(C)O\n",
      "Index 308: Pubchem_CID: 177, SMILES: CC=O\n",
      "Index 309: Pubchem_CID: 176, SMILES: CC(=O)O\n",
      "Index 310: Pubchem_CID: None, SMILES: None\n",
      "Index 311: Pubchem_CID: None, SMILES: None\n",
      "Index 312: Pubchem_CID: None, SMILES: None\n",
      "Index 313: Pubchem_CID: None, SMILES: None\n",
      "Index 314: Pubchem_CID: 73083158, SMILES: CC(=O)C1(CC2CCC1(C2(C)C)C)O\n",
      "Index 315: Pubchem_CID: 60961, SMILES: C1=NC(=C2C(=N1)N(C=N2)C3C(C(C(O3)CO)O)O)N\n",
      "Index 316: Pubchem_CID: 9905088, SMILES: CC1(CCCC2(C1CCC3(C2CC=C(CC=O)C=O)CO3)C)C\n",
      "Index 317: Pubchem_CID: 86597, SMILES: CC1=CCC(CC1)C(=CCC=C(C)C)C\n",
      "Index 318: Pubchem_CID: 5281520, SMILES: CC1=CCC(C=CCC(=CCC1)C)(C)C\n",
      "Index 319: Pubchem_CID: None, SMILES: None\n",
      "Index 320: Pubchem_CID: 442359, SMILES: CC1CCC(C2C13C2C(=CC3)C)C(C)C\n",
      "Index 321: Pubchem_CID: 92762, SMILES: CC1=CCCC2(C1CC(CC2)C(C)(C)O)C\n",
      "Index 322: Pubchem_CID: 5317844, SMILES: CC1CCC(CC2=C1CCC2C)C(=C)C\n",
      "Index 323: Pubchem_CID: 519324, SMILES: CC(=C)CCCC(=C)C=C\n",
      "Index 324: Pubchem_CID: None, SMILES: None\n",
      "Index 325: Pubchem_CID: 530421, SMILES: CC1CCCC2=CCC3C(C12C)C3(C)C\n",
      "Index 326: Pubchem_CID: 16211192, SMILES: CC1CCC2C1C3C(C3(C)C)CCC24CO4\n",
      "Index 327: Pubchem_CID: 7222, SMILES: C1=CC=C2C(=C1)N=CS2\n",
      "Index 328: Pubchem_CID: 91457, SMILES: CC12CCCC(=C)C1CC(CC2)C(C)(C)O\n",
      "Index 329: Pubchem_CID: 10104370, SMILES: CC1=CCC(CC1)C(=C)CCC=C(C)C\n",
      "Index 330: Pubchem_CID: 93081, SMILES: CC1CCC(C2C13C2C(=C)CC3)C(C)C\n",
      "Index 331: Pubchem_CID: 6450812, SMILES: CC1CCC2C(C2(C)C)C3C1CCC3=C\n",
      "Index 332: Pubchem_CID: 5318102, SMILES: CC1=CCC(C=CCC(=C)CCC1)(C)C\n",
      "Index 333: Pubchem_CID: 595133, SMILES: CC1(CC23C1CCC2(CCCC3=C)C)C\n",
      "Index 334: Pubchem_CID: 442347, SMILES: CC1=CCC2(CCC(C2(CC1)O)C(C)C)C\n",
      "Index 335: Pubchem_CID: 3001497, SMILES: C1C2C3C(C(C(O2)OC(=O)C4=CC(=C(C(=C4)O)O)O)OC(=O)C5=CC(=C(C6=C5C7C(=CC(=O)C(C7(O)O)(O6)O)C(=O)O3)O)O)OC(=O)C8=CC(=C(C(=C8C9=C(C(=C(C=C9C(=O)O1)O)O)O)O)O)O\n",
      "Index 336: Pubchem_CID: None, SMILES: None\n",
      "Index 337: Pubchem_CID: 91747494, SMILES: CC(=CCCC1(C2CCC1(C(C2)O)C)C)C\n",
      "Index 338: Pubchem_CID: 441589, SMILES: C1CN2CC3=CC4=C(C=C3C5C2C1=CCC5O)OCO4\n",
      "Index 339: Pubchem_CID: None, SMILES: None\n",
      "Index 340: Pubchem_CID: 6431015, SMILES: CC1CCC2C13CC=C(C(C3)C2(C)C)C\n",
      "Index 341: Pubchem_CID: 8078, SMILES: C1CCCCC1\n",
      "Index 342: Pubchem_CID: None, SMILES: None\n",
      "Index 343: Pubchem_CID: None, SMILES: None\n",
      "Index 344: Pubchem_CID: 5282457, SMILES: CCCCCC=CCC=CCCCCCCCC(=O)O\n",
      "Index 345: Pubchem_CID: 1549045, SMILES: CC=CC1=CC(=C(C=C1)OC)OC\n",
      "Index 346: Pubchem_CID: 1268143, SMILES: CC1(C2CCC(C1C2)(C)O)C\n",
      "Index 347: Pubchem_CID: None, SMILES: None\n",
      "Index 348: Pubchem_CID: None, SMILES: None\n",
      "Index 349: Pubchem_CID: 8842, SMILES: CC(CCC=C(C)C)CCO\n",
      "Index 350: Pubchem_CID: 108079, SMILES: C1CC1(NC(=O)CCC(C(=O)O)N)O\n",
      "Index 351: Pubchem_CID: 16132265, SMILES: CC(C)CC(C(=O)NC(CCC(=O)O)C(=O)NC(CC1=CC=CC=C1)C(=O)O)NC(=O)C2CCCN2C(=O)C(CC3=CC=CC=C3)NC(=O)C(C)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)C(CO)NC(=O)C(CCC(=O)O)NC(=O)C(CC(=O)O)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)CNC(=O)C(CC(=O)N)NC(=O)C4CCCN4C(=O)C(CC5=CC=C(C=C5)O)NC(=O)C(C(C)C)NC(=O)C(CCCCN)NC(=O)C(C(C)C)NC(=O)C6CCCN6C(=O)C(CCCNC(=N)N)NC(=O)C(CCCNC(=N)N)NC(=O)C(CCCCN)NC(=O)C(CCCCN)NC(=O)CNC(=O)C(C(C)C)NC(=O)C7CCCN7C(=O)C(CCCCN)NC(=O)CNC(=O)C(CC8=CNC9=CC=CC=C98)NC(=O)C(CCCNC(=N)N)NC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC1=CN=CN1)NC(=O)C(CCC(=O)O)NC(=O)C(CCSC)NC(=O)C(CO)NC(=O)C(CC1=CC=C(C=C1)O)NC(=O)C(CO)N\n",
      "Index 352: Pubchem_CID: None, SMILES: None\n",
      "Index 353: Pubchem_CID: 117443, SMILES: C1C(C(C(O1)O)CC2=CC3=C(C=C2)OCO3)CC4=CC5=C(C=C4)OCO5\n",
      "Index 354: Pubchem_CID: 637538, SMILES: C1CCCCCC=CCCCC1\n",
      "Index 355: Pubchem_CID: 70721, SMILES: C1CC1C(=O)C2CC2\n",
      "Index 356: Pubchem_CID: None, SMILES: None\n",
      "Index 357: Pubchem_CID: None, SMILES: None\n",
      "Index 358: Pubchem_CID: 5742590, SMILES: CCC(CCC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)OC5C(C(C(C(O5)CO)O)O)O)C)C)C(C)C\n",
      "Index 359: Pubchem_CID: 6552009, SMILES: CC1(C2CCC1(C(C2)O)C)C\n",
      "Index 360: Pubchem_CID: 37153, SMILES: C(CCCCCO)CCCCO\n",
      "Index 361: Pubchem_CID: None, SMILES: None\n",
      "Index 362: Pubchem_CID: 398788, SMILES: CN1CCC2=CC(=C(C3=C4C=C(C(=CC4=CC1=C23)OC)OC)OC)OC\n",
      "Index 363: Pubchem_CID: 5354238, SMILES: CC(=O)C=CC1=CC(=C(C=C1)O)OC\n",
      "Index 364: Pubchem_CID: None, SMILES: None\n",
      "Index 365: Pubchem_CID: 441005, SMILES: CC1=CC2C(CCC(=C2CC1)C)C(C)C\n",
      "Index 366: Pubchem_CID: 520383, SMILES: CC1=C2C=C(CCC2(CCC1)C)C(C)C\n",
      "Index 367: Pubchem_CID: 81722, SMILES: CC(C)(C1CCC(=C)CC1)O\n",
      "Index 368: Pubchem_CID: None, SMILES: None\n",
      "Index 369: Pubchem_CID: None, SMILES: None\n",
      "Index 370: Pubchem_CID: None, SMILES: None\n",
      "Index 371: Pubchem_CID: None, SMILES: None\n",
      "Index 372: Pubchem_CID: 534683, SMILES: CC1CCC2C13CC(C2(C)C)C(C4C3O4)C\n",
      "Index 373: Pubchem_CID: 9609, SMILES: CCSCC\n",
      "Index 374: Pubchem_CID: None, SMILES: None\n",
      "Index 375: Pubchem_CID: None, SMILES: None\n",
      "Index 376: Pubchem_CID: None, SMILES: None\n",
      "Index 377: Pubchem_CID: 62322, SMILES: CCO[Si](C)(C)OCC\n",
      "Index 378: Pubchem_CID: 8222, SMILES: CCCCCCCCCCCCCCCCCCCC\n",
      "Index 379: Pubchem_CID: 20054903, SMILES: CC=C(C=O)C1CC(=O)OC=C1C(=O)OC\n",
      "Index 380: Pubchem_CID: None, SMILES: None\n",
      "Index 381: Pubchem_CID: 591309, SMILES: CC(C)C1CCC(C(C1=O)C(=C)C)(C)C=C\n",
      "Index 382: Pubchem_CID: 12309744, SMILES: CC1CCC=C2C1(CC(CC2)C(=C)C)C\n",
      "Index 383: Pubchem_CID: 3283, SMILES: CCOCC\n",
      "Index 384: Pubchem_CID: 736681, SMILES: CCOC(=O)C=CC1=CC(=C(C=C1)O)OC\n",
      "Index 385: Pubchem_CID: 21228, SMILES: CCSC(C)C\n",
      "Index 386: Pubchem_CID: 442837, SMILES: CC12CCCC(C1CC(CC2)C(C)(C)O)(C)OC3=CC(=CC(=C3O)OC4=CC=C(C=C4)CC=C)CC=C\n",
      "Index 387: Pubchem_CID: 5317271, SMILES: CC(=O)CC(=O)C1=C(C=C(C=C1OC)OC)OC\n",
      "Index 388: Pubchem_CID: 107217, SMILES: CC(=O)OC1C(C2CCC1(C2)C)(C)C\n",
      "Index 389: Pubchem_CID: 94403, SMILES: CC(=CCCC(=CCCC(=CCOC(=O)C)C)C)C\n",
      "Index 390: Pubchem_CID: 638500, SMILES: CC(=CCCC(=CCCC(=CCOC(=O)C)C)C)C\n",
      "Index 391: Pubchem_CID: 102121497, SMILES: CN1CCC2=CC(=C3C=C2C1CC4=CC=C(C=C4)OC5=C(C=CC(=C5)CC6C7=C(O3)C(=C(C=C7CC[N+]6(C)[O-])OC)OC)OC)OC\n",
      "Index 392: Pubchem_CID: 636458, SMILES: CC1=CCC2=C(CC(=CCC1)C)OC=C2C\n",
      "Index 393: Pubchem_CID: 9651, SMILES: CN1CCC23C=CC(CC2OC4=C(C=CC(=C34)C1)OC)O\n",
      "Index 394: Pubchem_CID: 92313, SMILES: CC1=CC2C(CC1)C(=C)CCC2C(C)C\n",
      "Index 395: Pubchem_CID: 5315649, SMILES: CC(=CCCC1=CCCC(C1)C(=C)CCC=C(C)C)C\n",
      "Index 396: Pubchem_CID: 12304273, SMILES: CC1=CC=C(CC1)C(C)CCC=C(C)C\n",
      "Index 397: Pubchem_CID: 5282109, SMILES: CC(=CCCC(=CCOC=O)C)C\n",
      "Index 398: Pubchem_CID: 5317592, SMILES: COC1=CC(=CC(=C1O)OC)CCC=CC(=O)CCC2=CC(=C(C=C2)O)OC\n",
      "Index 399: Pubchem_CID: None, SMILES: None\n",
      "Index 400: Pubchem_CID: 10349562, SMILES: CCC=CCC=CCC=CCCCCCCCC(=O)OCC(COC1C(C(C(C(O1)COC2C(C(C(C(O2)CO)O)O)O)O)O)O)O\n",
      "Index 401: Pubchem_CID: None, SMILES: None\n",
      "Index 402: Pubchem_CID: 10009754, SMILES: CCCCCC=CCC=CCCCCCCCC(=O)OCC(COC1C(C(C(C(O1)COC2C(C(C(C(O2)CO)O)O)O)O)O)O)O\n",
      "Index 403: Pubchem_CID: None, SMILES: None\n",
      "Index 404: Pubchem_CID: None, SMILES: None\n",
      "Index 405: Pubchem_CID: 14274765, SMILES: COC1=CC(=CC2=C1OC(C2CO)C3=CC(=C(C=C3)O)OC)C=CC(=O)O\n",
      "Index 406: Pubchem_CID: 7860, SMILES: C(=O)C=O\n",
      "Index 407: Pubchem_CID: 5317768, SMILES: COC1=CC=C(C=C1)C(=O)C=CC2=C(C=C(C=C2)O)OC\n",
      "Index 408: Pubchem_CID: 227829, SMILES: CC1CCC(CC2=C1CCC2C)C(C)(C)O\n",
      "Index 409: Pubchem_CID: 12403, SMILES: CCCCCCCCCCCCCCCCCCCCC\n",
      "Index 410: Pubchem_CID: 12398, SMILES: CCCCCCCCCCCCCCCCC\n",
      "Index 411: Pubchem_CID: 8900, SMILES: CCCCCCC\n",
      "Index 412: Pubchem_CID: 5318039, SMILES: COC1=C(C=CC(=C1)CCC(CC(=O)CCC2=CC(=C(C=C2)O)OC)O)O\n",
      "Index 413: Pubchem_CID: None, SMILES: None\n",
      "Index 414: Pubchem_CID: None, SMILES: None\n",
      "Index 415: Pubchem_CID: 5318568, SMILES: COC1=CC(=CC(=C1O)OC)CCC(=O)C=CCCC2=CC(=C(C=C2)O)OC\n",
      "Index 416: Pubchem_CID: 170833, SMILES: CC1CCC(C(C1)O)C(=C)C\n",
      "Index 417: Pubchem_CID: 102442855, SMILES: COC1=C(C=CC(=C1)CC(C2CC(CC(O2)C3=CC(=C(C=C3)O)OC)O)O)O\n",
      "Index 418: Pubchem_CID: 15560069, SMILES: CC1=CCC(CC1)C(=C)CCC=C(C)CO\n",
      "Index 419: Pubchem_CID: 3893, SMILES: CCCCCCCCCCCC(=O)O\n",
      "Index 420: Pubchem_CID: 22311, SMILES: CC1=CCC(CC1)C(=C)C\n",
      "Index 421: Pubchem_CID: 6549, SMILES: CC(=CCCC(C)(C=C)O)C\n",
      "Index 422: Pubchem_CID: 6321405, SMILES: CC1(C2CCC1(C(C2)O)C)C\n",
      "Index 423: Pubchem_CID: 92874, SMILES: CC1=CC(=O)C2CC1C2(C)C\n",
      "Index 424: Pubchem_CID: 26447, SMILES: CC1CCC(C(=O)C1)C(C)C\n",
      "Index 425: Pubchem_CID: None, SMILES: None\n",
      "Index 426: Pubchem_CID: None, SMILES: None\n",
      "Index 427: Pubchem_CID: None, SMILES: None\n",
      "Index 428: Pubchem_CID: None, SMILES: None\n",
      "Index 429: Pubchem_CID: None, SMILES: None\n",
      "Index 430: Pubchem_CID: None, SMILES: None\n",
      "Index 431: Pubchem_CID: 6584, SMILES: CC(=O)OC\n",
      "Index 432: Pubchem_CID: None, SMILES: None\n",
      "Index 433: Pubchem_CID: 19844, SMILES: COC1=C(C=CC(=C1)C(=O)OC)O\n",
      "Index 434: Pubchem_CID: None, SMILES: None\n",
      "Index 435: Pubchem_CID: None, SMILES: None\n",
      "Index 436: Pubchem_CID: None, SMILES: None\n",
      "Index 437: Pubchem_CID: None, SMILES: None\n",
      "Index 438: Pubchem_CID: None, SMILES: None\n",
      "Index 439: Pubchem_CID: None, SMILES: None\n",
      "Index 440: Pubchem_CID: None, SMILES: None\n",
      "Index 441: Pubchem_CID: 5319726, SMILES: COC1=C(C=C(C=C1O)C(=O)OC)O\n",
      "Index 442: Pubchem_CID: 70697235, SMILES: CCCCCC(CC(=O)CCC1=CC(=C(C=C1)OC)OC)O\n",
      "Index 443: Pubchem_CID: None, SMILES: None\n",
      "Index 444: Pubchem_CID: 880, SMILES: CC(=O)C=O\n",
      "Index 445: Pubchem_CID: 31253, SMILES: CC(=CCCC(=C)C=C)C\n",
      "Index 446: Pubchem_CID: 5315565, SMILES: CCCCOC(=O)NC(=O)N\n",
      "Index 447: Pubchem_CID: 21630934, SMILES: CC1CCC(C(C1)OC(=O)C)C(C)C\n",
      "Index 448: Pubchem_CID: 15402, SMILES: CN(C)C1CSSC1\n",
      "Index 449: Pubchem_CID: 5275520, SMILES: CC(=CCCC(=CC(=O)O)C)C\n",
      "Index 450: Pubchem_CID: 5320129, SMILES: CC(=CCC1=CC(=CC(=C1O)CC=C(C)C)C(=O)O)C\n",
      "Index 451: Pubchem_CID: 5365982, SMILES: CCC(=O)OCC=C(C)CCC=C(C)C\n",
      "Index 452: Pubchem_CID: 243696, SMILES: CCCCCCCCCCCCCCCCCCCCCCCCCCCCCO\n",
      "Index 453: Pubchem_CID: 110745, SMILES: CC1CCC2(C1C3C(C3(C)C)CCC2C)O\n",
      "Index 454: Pubchem_CID: 441575, SMILES: CC1CC=CC(=O)O1\n",
      "Index 455: Pubchem_CID: 442980, SMILES: CC1C2CCC3C2(CCC4C3CC=C5C4(CCC(C5)NC)C)C(=O)O1\n",
      "Index 456: Pubchem_CID: 8063, SMILES: CCCCC=O\n",
      "Index 457: Pubchem_CID: None, SMILES: None\n",
      "Index 458: Pubchem_CID: 68381, SMILES: CC(C)CCC(=O)C1=COC=C1\n",
      "Index 459: Pubchem_CID: None, SMILES: None\n",
      "Index 460: Pubchem_CID: None, SMILES: None\n",
      "Index 461: Pubchem_CID: 849, SMILES: C1CCNC(C1)C(=O)O\n",
      "Index 462: Pubchem_CID: 519323, SMILES: CC1=CCC(C=C1)C(C)(C)O\n",
      "Index 463: Pubchem_CID: 441942, SMILES: CC1(CC2C3=CC(C4C5(CCC(=O)C(C5CCC4(C3(CCC2(CC1=O)C(=O)O)C)C)(C)C)C)O)C\n",
      "Index 464: Pubchem_CID: 527, SMILES: CCC=O\n",
      "Index 465: Pubchem_CID: 1032, SMILES: CCC(=O)O\n",
      "Index 466: Pubchem_CID: 636871, SMILES: COC1=C(C=CC(=C1)CC2C(CC(O2)CC(=O)C3=CC(=C(C=C3)O)OC)O)O\n",
      "Index 467: Pubchem_CID: 44245542, SMILES: CC1(C(CC2=C(O1)C(=O)C3=CC=CC=C3C2=O)O)C\n",
      "Index 468: Pubchem_CID: 101857710, SMILES: CC=C(C)CCC=C(C)C(=O)OC1CC2=C(C(=O)C3=CC=CC=C3C2=O)OC1(C)C\n",
      "Index 469: Pubchem_CID: 6474554, SMILES: CC=C(C)CCC=C(C)C(=O)OCC(C)(C)CC1=C(C2=CC=CC=C2C(=O)C1=O)O\n",
      "Index 470: Pubchem_CID: 465430, SMILES: CC(C)(CC1=C(C2=CC=CC=C2C(=O)C1=O)O)COC(=O)C3=CC4=C(C=C3)OCO4\n",
      "Index 471: Pubchem_CID: 10622350, SMILES: CC(=CCCC(C)(C=C)O)C(=O)OCC(C)(C)CC1=C(C2=CC=CC=C2C(=O)C1=O)O\n",
      "Index 472: Pubchem_CID: 10764908, SMILES: CC(C(=C)CCC=C(C)C(=O)OCC(C)(C)CC1=C(C2=CC=CC=C2C(=O)C1=O)O)O\n",
      "Index 473: Pubchem_CID: 10765714, SMILES: CC(C(C)(CCC=C(C)C(=O)OCC(C)(C)CC1=C(C2=CC=CC=C2C(=O)C1=O)O)O)O\n",
      "Index 474: Pubchem_CID: 10808757, SMILES: CC(C)(CC1=C(C2=CC=CC=C2C(=O)C1=O)O)COC(=O)C3=CC=CC=C3\n",
      "Index 475: Pubchem_CID: 10766384, SMILES: CC(C)(CC1=C(C2=CC=CC=C2C(=O)C1=O)O)COC(=O)C3=C(C4=CC=CC=C4C(=C3)OC)O\n",
      "Index 476: Pubchem_CID: 10005113, SMILES: CC(C)(CC1=C(C2=CC=CC=C2C(=O)C1=O)O)COC(=O)C3=C(C4=CC=CC=C4C(=C3)OC)OC\n",
      "Index 477: Pubchem_CID: None, SMILES: None\n",
      "Index 478: Pubchem_CID: 6442707, SMILES: CC=CC#CC#CC#CC=CC(CO)O\n",
      "Index 479: Pubchem_CID: 188297, SMILES: CC1=CCC(C2(C1C3C(CC2)C(=C)C(=O)O3)C)O\n",
      "Index 480: Pubchem_CID: 90488848, SMILES: CC(C)CC(C(=O)NC(CCC(=O)O)C(=O)NC(CC1=CC=CC=C1)C(=O)O)NC(=O)C2CCCN2C(=O)C(CC3=CC=CC=C3)NC(=O)C(C)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)C(CO)NC(=O)C(CCC(=O)O)NC(=O)C(CC(=O)O)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)CNC(=O)C(CC(=O)N)NC(=O)C4CCCN4C(=O)C(CC5=CC=C(C=C5)O)NC(=O)C(C(C)C)NC(=O)C(CCCCN)NC(=O)C(C(C)C)NC(=O)C6CCCN6C(=O)C(CCCNC(=N)N)NC(=O)C(CCCNC(=N)N)NC(=O)C(CCCCN)NC(=O)C(CCCCN)NC(=O)CNC(=O)C(C(C)C)NC(=O)C7CCCN7C(=O)C(CCCCN)NC(=O)CNC(=O)C(CC8=CNC9=CC=CC=C98)NC(=O)C(CCCNC(=N)N)NC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC1=CNC=N1)NC(=O)C(CCC(=O)O)NC(=O)C(CCSC)NC(=O)C(CO)NC(=O)C(CC1=CC=C(C=C1)O)NC(=O)C(CO)N\n",
      "Index 481: Pubchem_CID: 5281698, SMILES: COC1=C(C=C(C2=C1OC(=C(C2=O)O)C3=CC=C(C=C3)O)O)O\n",
      "Index 482: Pubchem_CID: 5321293, SMILES: CC(C)C1CCC(C(C1=O)C(=C)C)(C)C=C\n",
      "Index 483: Pubchem_CID: None, SMILES: None\n",
      "Index 484: Pubchem_CID: 5280794, SMILES: CCC(C=CC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C\n",
      "Index 485: Pubchem_CID: 7501, SMILES: C=CC1=CC=CC=C1\n",
      "Index 486: Pubchem_CID: 3084331, SMILES: CC1=CC2C(CCC(C2CC1)(C)O)C(C)C\n",
      "Index 487: Pubchem_CID: 79035, SMILES: CC1(C2CC3C1(C3C2)C)C\n",
      "Index 488: Pubchem_CID: 442926, SMILES: CC(C)CC(=O)C1=C(C23CC(C(C2CC(=O)C3(C1=O)O)(C)C)C(=C)C)O\n",
      "Index 489: Pubchem_CID: 61126, SMILES: CC1=CC(C2CC1C2(C)C)O\n",
      "Index 490: Pubchem_CID: 93135, SMILES: CC1=C(C=C(C=C1)C(C)CCC=C(C)C)O\n",
      "Index 491: Pubchem_CID: 7758, SMILES: CCC(C)OC(=O)C\n",
      "Index 492: Pubchem_CID: None, SMILES: None\n",
      "Index 493: Pubchem_CID: None, SMILES: None\n",
      "Index 494: Pubchem_CID: 6010, SMILES: CC12CCC(=O)C=C1CCC3C2CCC4(C3CCC4(C)O)C\n",
      "Index 495: Pubchem_CID: 3016110, SMILES: CC(CCC1=CC(=C(C=C1)O)OC)O\n",
      "Index 496: Pubchem_CID: 92776, SMILES: CC1=CCC(C=C1)C(C)CCC=C(C)C\n",
      "Index 497: Pubchem_CID: 5317270, SMILES: CC12CCCC(=C)C1CC(CC2)C(C)(C)O\n",
      "Index 498: Pubchem_CID: None, SMILES: None\n",
      "Index 499: Pubchem_CID: None, SMILES: None\n",
      "Index 500: Pubchem_CID: None, SMILES: None\n",
      "Index 501: Pubchem_CID: None, SMILES: None\n",
      "Index 502: Pubchem_CID: 3083781, SMILES: CC1CCC2C(C3C(CN2C1)C4CC56C(C4(C(C3O)O)O)CCC7C5(CCC(C7(O6)O)OC(=O)C)C)(C)O\n",
      "Index 503: Pubchem_CID: None, SMILES: None\n",
      "Index 504: Pubchem_CID: None, SMILES: None\n",
      "Index 505: Pubchem_CID: None, SMILES: None\n",
      "Index 506: Pubchem_CID: None, SMILES: None\n",
      "Index 507: Pubchem_CID: None, SMILES: None\n",
      "Index 508: Pubchem_CID: None, SMILES: None\n",
      "Index 509: Pubchem_CID: None, SMILES: None\n",
      "Index 510: Pubchem_CID: 11363, SMILES: CC(=O)C(=O)C1=CC=CC=C1\n",
      "Index 511: Pubchem_CID: None, SMILES: None\n",
      "Index 512: Pubchem_CID: None, SMILES: None\n",
      "Index 513: Pubchem_CID: 5322112, SMILES: CC1C(OC(N1C)C)C2=CC=CC=C2\n",
      "Index 514: Pubchem_CID: None, SMILES: None\n",
      "Index 515: Pubchem_CID: 572948, SMILES: CC=C1CCCC1(C)C\n",
      "Index 516: Pubchem_CID: None, SMILES: None\n",
      "Index 517: Pubchem_CID: None, SMILES: None\n",
      "Index 518: Pubchem_CID: 5321950, SMILES: CC=C(C)C=O\n",
      "Index 519: Pubchem_CID: 434666, SMILES: CC1C(OCN1C)C2=CC=CC=C2\n",
      "Index 520: Pubchem_CID: 6544, SMILES: CC1=CC(=O)CC(C1)(C)C\n",
      "Index 521: Pubchem_CID: None, SMILES: None\n",
      "Index 522: Pubchem_CID: None, SMILES: None\n",
      "Index 523: Pubchem_CID: None, SMILES: None\n",
      "Index 524: Pubchem_CID: None, SMILES: None\n",
      "Index 525: Pubchem_CID: None, SMILES: None\n",
      "Index 526: Pubchem_CID: None, SMILES: None\n",
      "Index 527: Pubchem_CID: None, SMILES: None\n",
      "Index 528: Pubchem_CID: None, SMILES: None\n",
      "Index 529: Pubchem_CID: None, SMILES: None\n",
      "Index 530: Pubchem_CID: 440752, SMILES: C1=CC2=C(C=C1O)C(=O)C=C(N2)C(=O)O\n",
      "Index 531: Pubchem_CID: None, SMILES: None\n",
      "Index 532: Pubchem_CID: None, SMILES: None\n",
      "Index 533: Pubchem_CID: 72941763, SMILES: CC1CC2(C3C(O3)(CO2)C)OC4C1C5(C(CC67CC68CCC(C(C8CC=C7C5(C4)C)(C)C)OC9C(C(C(CO9)O)O)O)OC(=O)C)C\n",
      "Index 534: Pubchem_CID: 5280704, SMILES: C1=CC(=CC=C1C2=CC(=O)C3=C(C=C(C=C3O2)OC4C(C(C(C(O4)CO)O)O)O)O)O\n",
      "Index 535: Pubchem_CID: None, SMILES: None\n",
      "Index 536: Pubchem_CID: 689043, SMILES: C1=CC(=C(C=C1C=CC(=O)O)O)O\n",
      "Index 537: Pubchem_CID: 10363, SMILES: CC1CCC(=CC1=O)C(C)C\n",
      "Index 538: Pubchem_CID: None, SMILES: None\n",
      "Index 539: Pubchem_CID: None, SMILES: None\n",
      "Index 540: Pubchem_CID: 16020000, SMILES: CC1CC2C(OC3(C1C4(CCC56CC57CCC(C(C7CCC6C4(C3O)C)(C)C)O)C)O2)C(C)(C)O\n",
      "Index 541: Pubchem_CID: 12851884, SMILES: C1C2C1C(NC2)C(=O)O\n",
      "Index 542: Pubchem_CID: 85567, SMILES: CC1=CC(C(CC1)C(C)C)O\n",
      "Index 543: Pubchem_CID: None, SMILES: None\n",
      "Index 544: Pubchem_CID: None, SMILES: None\n",
      "Index 545: Pubchem_CID: 129628549, SMILES: C1=C(C=C(C(=C1O)O)O)C(=O)C(C(C(C(C(C=O)O)O)O)O)(C(=O)C2=CC(=C(C(=C2)O)O)O)O\n",
      "Index 546: Pubchem_CID: 7059596, SMILES: CC(C(C1=CC=CC=C1)O)N(C)C\n",
      "Index 547: Pubchem_CID: 7028, SMILES: CC(C(C1=CC=CC=C1)O)NC\n",
      "Index 548: Pubchem_CID: None, SMILES: None\n",
      "Index 549: Pubchem_CID: None, SMILES: None\n",
      "Index 550: Pubchem_CID: None, SMILES: None\n",
      "Index 551: Pubchem_CID: None, SMILES: None\n",
      "Index 552: Pubchem_CID: None, SMILES: None\n",
      "Index 553: Pubchem_CID: 21676348, SMILES: C1=CC(=CC=C1C2=C(C(=O)C3=C(O2)C4=C(C=C3O)OC5(C(C4C6=C(C=C(C=C6O5)O)O)O)C7=CC=C(C=C7)O)O)O\n",
      "Index 554: Pubchem_CID: 25051177, SMILES: C1C2C3=C(C=C(C=C3OC1(OC4=C2C5=C(C(=C4)O)C(=O)C(=C(O5)C6=CC=C(C=C6)O)O)C7=CC=C(C=C7)O)O)O\n",
      "Index 555: Pubchem_CID: 161171, SMILES: CC1C(OC(=O)N1C)C2=CC=CC=C2\n",
      "Index 556: Pubchem_CID: 7799, SMILES: CCCCCCCC(=O)OCC\n",
      "Index 557: Pubchem_CID: 10401784, SMILES: COC1=C(C=CC(=C1)C=CC(=O)NCCC2=CN=CN2)O\n",
      "Index 558: Pubchem_CID: None, SMILES: None\n",
      "Index 559: Pubchem_CID: 5280544, SMILES: C1=CC(=CC=C1C2=C(C(=O)C3=C(O2)C(=C(C=C3O)O)O)O)O\n",
      "Index 560: Pubchem_CID: None, SMILES: None\n",
      "Index 561: Pubchem_CID: 44259932, SMILES: C1=CC(=CC=C1C2=C(C(=O)C3=C(O2)C(=C(C=C3O)OC4C(C(C(C(O4)CO)O)O)O)O)O)O\n",
      "Index 562: Pubchem_CID: None, SMILES: None\n",
      "Index 563: Pubchem_CID: None, SMILES: None\n",
      "Index 564: Pubchem_CID: None, SMILES: None\n",
      "Index 565: Pubchem_CID: None, SMILES: None\n",
      "Index 566: Pubchem_CID: 44258812, SMILES: CC1C(C(C(C(O1)OC2=CC(=C3C(=C2)OC(=C(C3=O)OC4C(C(C(C(O4)CO)O)O)O)C5=CC=C(C=C5)O)O)O)O)O\n",
      "Index 567: Pubchem_CID: 3845, SMILES: C1=CC=C2C(=C1)C(=O)C=C(N2)C(=O)O\n",
      "Index 568: Pubchem_CID: 3081374, SMILES: C1=C(C=C(C(=C1O)O)O)C2C(C(C3=C(C=C(C=C3O2)O)O)O)O\n",
      "Index 569: Pubchem_CID: None, SMILES: None\n",
      "Index 570: Pubchem_CID: 10297, SMILES: CC(C(C1=CC=CC=C1)O)N\n",
      "Index 571: Pubchem_CID: None, SMILES: None\n",
      "Index 572: Pubchem_CID: 5317052, SMILES: C1C(C(OC2=C1C(=CC3=C2C4C(C(O3)(OC5=CC(=CC(=C45)O)O)C6=CC=C(C=C6)O)O)O)C7=CC=C(C=C7)O)O\n",
      "Index 573: Pubchem_CID: None, SMILES: None\n",
      "Index 574: Pubchem_CID: 159133, SMILES: C1C(C(OC2=CC3=C(C4C(C(O3)(OC5=CC(=CC(=C45)O)O)C6=CC=C(C=C6)O)O)C(=C21)O)C7=CC=C(C=C7)O)O\n",
      "Index 575: Pubchem_CID: None, SMILES: None\n",
      "Index 576: Pubchem_CID: None, SMILES: None\n",
      "Index 577: Pubchem_CID: None, SMILES: None\n",
      "Index 578: Pubchem_CID: None, SMILES: None\n",
      "Index 579: Pubchem_CID: None, SMILES: None\n",
      "Index 580: Pubchem_CID: 54704413, SMILES: C[N+](C)(C)CCC1=C(C=C(C=C1)[O-])C(=O)O\n",
      "Index 581: Pubchem_CID: 8139, SMILES: CCCCCCCCCCCC(=O)OC\n",
      "Index 582: Pubchem_CID: 7148, SMILES: CCC(=O)C1=CC=CC=C1\n",
      "Index 583: Pubchem_CID: 64782, SMILES: CC(C(C1=CC=CC=C1)O)N(C)C\n",
      "Index 584: Pubchem_CID: 57391548, SMILES: CC(=CCC1=C(C=CC(=C1)OC2C(C(C(C(O2)CO)O)O)O)O)C\n",
      "Index 585: Pubchem_CID: 163184033, SMILES: C1=CC=C(C(=C1)C=CC(=O)O)OC2C(C(C(C(O2)CO)O)O)O\n",
      "Index 586: Pubchem_CID: None, SMILES: None\n",
      "Index 587: Pubchem_CID: 564463, SMILES: CC(C)C1CCC(C=C1)CO\n",
      "Index 588: Pubchem_CID: 5281643, SMILES: C1=CC(=C(C=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)OC4C(C(C(C(O4)CO)O)O)O)O)O\n",
      "Index 589: Pubchem_CID: 5280804, SMILES: C1=CC(=C(C=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)OC4C(C(C(C(O4)CO)O)O)O)O)O\n",
      "Index 590: Pubchem_CID: None, SMILES: None\n",
      "Index 591: Pubchem_CID: 7124, SMILES: C1=CC=C2C(=C1)C=CC(=N2)C(=O)O\n",
      "Index 592: Pubchem_CID: None, SMILES: None\n",
      "Index 593: Pubchem_CID: 101767126, SMILES: COC1=CC(=CC2=C1OC(C2CO)C3=CC(=C(C=C3)O)OC)CC(CO)C(CC4=CC(=C(C=C4)O)OC)CO\n",
      "Index 594: Pubchem_CID: None, SMILES: None\n",
      "Index 595: Pubchem_CID: 91700324, SMILES: CC(C)C1CCC(C=C1)CO\n",
      "Index 596: Pubchem_CID: None, SMILES: None\n",
      "Index 597: Pubchem_CID: None, SMILES: None\n",
      "Index 598: Pubchem_CID: 530422, SMILES: CC1CC2C(C2(C)C)C=C1\n",
      "Index 599: Pubchem_CID: 641612, SMILES: CC1CCCC1C\n",
      "Index 600: Pubchem_CID: None, SMILES: None\n",
      "Index 601: Pubchem_CID: None, SMILES: None\n",
      "Index 602: Pubchem_CID: None, SMILES: None\n",
      "Index 603: Pubchem_CID: None, SMILES: None\n",
      "Index 604: Pubchem_CID: 197678, SMILES: CC(=CCC1=C(C=CC(=C1)C2CC(=O)C3=C(O2)C4=C(C=C3)OC(C=C4)(C)C)O)C\n",
      "Index 605: Pubchem_CID: 22810194, SMILES: CCC(C)C(C)C\n",
      "Index 606: Pubchem_CID: 11869658, SMILES: CC1(CCC2(CCC3(C(=CCC4C3(CCC5C4(CCC(C5(C)C)O)C)C)C2C1)C)C(=O)O)C\n",
      "Index 607: Pubchem_CID: 22810193, SMILES: CCC(C)CC(C)C\n",
      "Index 608: Pubchem_CID: 10881804, SMILES: CC1(C=CC2=C(O1)C=CC(=C2)C=CC(=O)C3=C(C=C(C=C3)O)O)C\n",
      "Index 609: Pubchem_CID: None, SMILES: None\n",
      "Index 610: Pubchem_CID: None, SMILES: None\n",
      "Index 611: Pubchem_CID: 5316802, SMILES: CC(=CCC1=C(C=CC(=C1)C=CC(=O)C2=C(C(=C(C=C2)O)CC=C(C)C)O)O)C\n",
      "Index 612: Pubchem_CID: 5935664, SMILES: CC(C)C=CC1=CC(=C(C=C1O)O)C(=O)C=CC2=CC(=C(C=C2)O)C=CC(C)C\n",
      "Index 613: Pubchem_CID: None, SMILES: None\n",
      "Index 614: Pubchem_CID: 11267805, SMILES: CC(=CCC1=C(C(=CC(=C1)C=CC(=O)C2=C(C=C(C=C2)O)O)O)O)C\n",
      "Index 615: Pubchem_CID: None, SMILES: None\n",
      "Index 616: Pubchem_CID: 443162, SMILES: CC1=CCC(CC1)C(C)(C)O\n",
      "Index 617: Pubchem_CID: None, SMILES: None\n",
      "Index 618: Pubchem_CID: None, SMILES: None\n",
      "Index 619: Pubchem_CID: None, SMILES: None\n",
      "Index 620: Pubchem_CID: None, SMILES: None\n",
      "Index 621: Pubchem_CID: None, SMILES: None\n",
      "Index 622: Pubchem_CID: None, SMILES: None\n",
      "Index 623: Pubchem_CID: 7237, SMILES: CC1=CC=CC=C1C\n",
      "Index 624: Pubchem_CID: None, SMILES: None\n",
      "Index 625: Pubchem_CID: None, SMILES: None\n",
      "Index 626: Pubchem_CID: 17903416, SMILES: CCC(C)CCCCCCCCCCC(=O)[O-]\n",
      "Index 627: Pubchem_CID: 73398, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(CC5)(C)C(=O)O)C)C)C)C\n",
      "Index 628: Pubchem_CID: None, SMILES: None\n",
      "Index 629: Pubchem_CID: None, SMILES: None\n",
      "Index 630: Pubchem_CID: 263, SMILES: CCCCO\n",
      "Index 631: Pubchem_CID: 480872, SMILES: CC(=CCC1=CC2=C(C=C1O)OC3C2COC4=C3C(=C(C(=C4)O)CC=C(C)C)OC)C\n",
      "Index 632: Pubchem_CID: 480873, SMILES: CC(=CCC1=C(C=CC2=C1OC3C2COC4=C3C(=CC(=C4)O)OC)O)C\n",
      "Index 633: Pubchem_CID: None, SMILES: None\n",
      "Index 634: Pubchem_CID: 11542, SMILES: CCCC(C)(C)C\n",
      "Index 635: Pubchem_CID: None, SMILES: None\n",
      "Index 636: Pubchem_CID: 11447, SMILES: CCCC(C)C(C)C\n",
      "Index 637: Pubchem_CID: 69906284, SMILES: CC(=CCC1=C(C=CC(=C1O)C(=O)C=CC2=C(C=C(C=C2)O)O)O)C\n",
      "Index 638: Pubchem_CID: 5057077, SMILES: C1=CC(=CC=C1C(=O)C=CC2=C(C=C(C=C2)O)O)O\n",
      "Index 639: Pubchem_CID: None, SMILES: None\n",
      "Index 640: Pubchem_CID: 11045, SMILES: C1=C(C=C(C(=C1[N+](=O)[O-])S(=O)(=O)O)[N+](=O)[O-])[N+](=O)[O-]\n",
      "Index 641: Pubchem_CID: 5376979, SMILES: C1=CC=C(C=C1)C=CC(=O)C2=C(C=C(C=C2)O)O\n",
      "Index 642: Pubchem_CID: 124702, SMILES: C(C1C(C(C(N1)CO)O)O)O\n",
      "Index 643: Pubchem_CID: None, SMILES: None\n",
      "Index 644: Pubchem_CID: None, SMILES: None\n",
      "Index 645: Pubchem_CID: None, SMILES: None\n",
      "Index 646: Pubchem_CID: None, SMILES: None\n",
      "Index 647: Pubchem_CID: 222284, SMILES: CCC(CCC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C\n",
      "Index 648: Pubchem_CID: None, SMILES: None\n",
      "Index 649: Pubchem_CID: None, SMILES: None\n",
      "Index 650: Pubchem_CID: 19814101, SMILES: CC12CCC(CC1C3=CC(=O)C4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)O)C)(C)C(=O)O\n",
      "Index 651: Pubchem_CID: 556516, SMILES: CC1(C2C1C=C(CC2)C=O)C\n",
      "Index 652: Pubchem_CID: 5319688, SMILES: COC1=C(C=CC(=C1)O)C(=O)C=CC2=CC=C(C=C2)O\n",
      "Index 653: Pubchem_CID: None, SMILES: None\n",
      "Index 654: Pubchem_CID: None, SMILES: None\n",
      "Index 655: Pubchem_CID: None, SMILES: None\n",
      "Index 656: Pubchem_CID: 75364, SMILES: CCCCCCCCCCCCC(=O)C\n",
      "Index 657: Pubchem_CID: None, SMILES: None\n",
      "Index 658: Pubchem_CID: 10378419, SMILES: COC1=CC(=C(C=C1)C2=CC3=C(C=C(C=C3)O)OC2)O\n",
      "Index 659: Pubchem_CID: None, SMILES: None\n",
      "Index 660: Pubchem_CID: 480785, SMILES: CC(=CCC1=C(C=CC(=C1O)C2COC3=C(C(=CC(=C3C2=O)O)O)CC=C(C)C)O)C\n",
      "Index 661: Pubchem_CID: 195396, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC6(CC5OC6=O)C)C)C(=O)OC)C)C\n",
      "Index 662: Pubchem_CID: 11229, SMILES: CCC(C)(C)CC\n",
      "Index 663: Pubchem_CID: None, SMILES: None\n",
      "Index 664: Pubchem_CID: 6478421, SMILES: COC1=C(C=CC(=C1O)O)C=CC(=O)C2=CC(=C(C=C2)O)O\n",
      "Index 665: Pubchem_CID: None, SMILES: None\n",
      "Index 666: Pubchem_CID: 13965473, SMILES: COC1=C(C=C(C=C1)C2=COC3=CC(=C(C=C3C2=O)OC)O)O\n",
      "Index 667: Pubchem_CID: None, SMILES: None\n",
      "Index 668: Pubchem_CID: 10434225, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)CC=C4C3(CCC5(C4CC(CC5)(C)C(=O)O)C)C)C)C\n",
      "Index 669: Pubchem_CID: 12048, SMILES: CCC(CC)CC\n",
      "Index 670: Pubchem_CID: 15228662, SMILES: CC1(C=CC2=C(O1)C=CC3=C2OCC(C3)C4=C(C(=C(C=C4)OC)O)O)C\n",
      "Index 671: Pubchem_CID: 480854, SMILES: CC(=CCC1=C(C=CC(=C1)C2C(C(=O)C3=C(O2)C(=C(C=C3)O)CC=C(C)C)O)O)C\n",
      "Index 672: Pubchem_CID: 480854, SMILES: CC(=CCC1=C(C=CC(=C1)C2C(C(=O)C3=C(O2)C(=C(C=C3)O)CC=C(C)C)O)O)C\n",
      "Index 673: Pubchem_CID: None, SMILES: None\n",
      "Index 674: Pubchem_CID: None, SMILES: None\n",
      "Index 675: Pubchem_CID: 5319439, SMILES: CC1(C=CC2=C(O1)C=CC3=C2OCC(C3)C4=C(C(=C(C=C4)O)OC)O)C\n",
      "Index 676: Pubchem_CID: None, SMILES: None\n",
      "Index 677: Pubchem_CID: 11519, SMILES: CCCCC(C)CC\n",
      "Index 678: Pubchem_CID: 11507, SMILES: CCCC(C)CC\n",
      "Index 679: Pubchem_CID: 7282, SMILES: CCC(C)CC\n",
      "Index 680: Pubchem_CID: None, SMILES: None\n",
      "Index 681: Pubchem_CID: None, SMILES: None\n",
      "Index 682: Pubchem_CID: None, SMILES: None\n",
      "Index 683: Pubchem_CID: 14632193, SMILES: C1=CC(=CC=C1CC(C(=O)C2=C(C=C(C=C2)O)O)O)O\n",
      "Index 684: Pubchem_CID: None, SMILES: None\n",
      "Index 685: Pubchem_CID: None, SMILES: None\n",
      "Index 686: Pubchem_CID: 238782, SMILES: C1C(OC2=CC(=CC(=C2C1=O)O)O)C3=CC=CC=C3\n",
      "Index 687: Pubchem_CID: 3385, SMILES: C1=C(C(=O)NC(=O)N1)F\n",
      "Index 688: Pubchem_CID: None, SMILES: None\n",
      "Index 689: Pubchem_CID: None, SMILES: None\n",
      "Index 690: Pubchem_CID: None, SMILES: None\n",
      "Index 691: Pubchem_CID: None, SMILES: None\n",
      "Index 692: Pubchem_CID: None, SMILES: None\n",
      "Index 693: Pubchem_CID: 25015742, SMILES: COC1=CC(=CC2=C1C=C(C(=O)O2)C3=C(C=C(C=C3)O)O)O\n",
      "Index 694: Pubchem_CID: 268208, SMILES: CC1=C(C(=O)C2=C(O1)C=C(C=C2)OC(=O)C)C3=CC=CC=C3\n",
      "Index 695: Pubchem_CID: None, SMILES: None\n",
      "Index 696: Pubchem_CID: None, SMILES: None\n",
      "Index 697: Pubchem_CID: None, SMILES: None\n",
      "Index 698: Pubchem_CID: None, SMILES: None\n",
      "Index 699: Pubchem_CID: 24832676, SMILES: CCN1CC2(C(CC(C34C2C(C(C31)C5(C6C4CC(C6OC(=O)C7=CC=CC=C7)(C(C5O)OC)O)OC(=O)C)OC)OC)O)COC\n",
      "Index 700: Pubchem_CID: None, SMILES: None\n",
      "Index 701: Pubchem_CID: None, SMILES: None\n",
      "Index 702: Pubchem_CID: 195342, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(CO5)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 703: Pubchem_CID: None, SMILES: None\n",
      "Index 704: Pubchem_CID: 5481962, SMILES: CC(=CCC1=C(OC2=C(C1=O)C(=CC3=C2C=CC(O3)(C)C)O)C4=CC(=C(C=C4O)O)O)C\n",
      "Index 705: Pubchem_CID: None, SMILES: None\n",
      "Index 706: Pubchem_CID: 64971, SMILES: CC(=C)C1CCC2(C1C3CCC4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)C)O)C)C(=O)O\n",
      "Index 707: Pubchem_CID: 31101, SMILES: CC(C)CC1C(=O)N2CCCC2C3(N1C(=O)C(O3)(C(C)C)NC(=O)C4CN(C5CC6=C(NC7=CC=CC(=C67)C5=C4)Br)C)O\n",
      "Index 708: Pubchem_CID: 31100, SMILES: CC(C)CC1C(=O)N2CCCC2C3(N1C(=O)C(O3)(C(C)C)NC(=O)C4CN(C5CC6=C(NC7=CC=CC(=C67)C5=C4)Br)C)O.CS(=O)(=O)O\n",
      "Index 709: Pubchem_CID: 86480, SMILES: CCCC(CCC)C(=O)[O-].CCCC(CCC)C(=O)[O-].[Ca+2]\n",
      "Index 710: Pubchem_CID: 16132265, SMILES: CC(C)CC(C(=O)NC(CCC(=O)O)C(=O)NC(CC1=CC=CC=C1)C(=O)O)NC(=O)C2CCCN2C(=O)C(CC3=CC=CC=C3)NC(=O)C(C)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)C(CO)NC(=O)C(CCC(=O)O)NC(=O)C(CC(=O)O)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)CNC(=O)C(CC(=O)N)NC(=O)C4CCCN4C(=O)C(CC5=CC=C(C=C5)O)NC(=O)C(C(C)C)NC(=O)C(CCCCN)NC(=O)C(C(C)C)NC(=O)C6CCCN6C(=O)C(CCCNC(=N)N)NC(=O)C(CCCNC(=N)N)NC(=O)C(CCCCN)NC(=O)C(CCCCN)NC(=O)CNC(=O)C(C(C)C)NC(=O)C7CCCN7C(=O)C(CCCCN)NC(=O)CNC(=O)C(CC8=CNC9=CC=CC=C98)NC(=O)C(CCCNC(=N)N)NC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC1=CN=CN1)NC(=O)C(CCC(=O)O)NC(=O)C(CCSC)NC(=O)C(CO)NC(=O)C(CC1=CC=C(C=C1)O)NC(=O)C(CO)N\n",
      "Index 711: Pubchem_CID: 5281255, SMILES: CC(=CCC1=C(C=CC(=C1O)C(=O)C=CC2=CC=C(C=C2)O)O)C\n",
      "Index 712: Pubchem_CID: None, SMILES: None\n",
      "Index 713: Pubchem_CID: 135398738, SMILES: CN(C)N=NC1=C(NC=N1)C(=O)N\n",
      "Index 714: Pubchem_CID: 480775, SMILES: CC(=CCC1=C(C2=C(C=C1O)OCC(=C2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 715: Pubchem_CID: 10109594, SMILES: CC(=CCC1=C(C=C2C(=C1OC)C=C(CO2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 716: Pubchem_CID: None, SMILES: None\n",
      "Index 717: Pubchem_CID: 5743, SMILES: CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\n",
      "Index 718: Pubchem_CID: None, SMILES: None\n",
      "Index 719: Pubchem_CID: None, SMILES: None\n",
      "Index 720: Pubchem_CID: 656852, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)[O-])O)O)OC5C(C(C(C(O5)C(=O)[O-])O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C.[K+].[K+]\n",
      "Index 721: Pubchem_CID: None, SMILES: None\n",
      "Index 722: Pubchem_CID: 23663956, SMILES: CCCC(CCC)C(=O)O.CCCC(CCC)C(=O)[O-].[Na+]\n",
      "Index 723: Pubchem_CID: 439630, SMILES: C1(C(C(OC(C1O)O)C(=O)O)O)O\n",
      "Index 724: Pubchem_CID: 5316952, SMILES: CCCCCCCCCCCCCCCCCCCCCCOC(=O)C=CC1=CC(=C(C=C1)O)O\n",
      "Index 725: Pubchem_CID: 7500, SMILES: CCC1=CC=CC=C1\n",
      "Index 726: Pubchem_CID: 6442675, SMILES: COC1=C(C=CC(=C1)O)C=CC(=O)C2=CC=C(C=C2)O\n",
      "Index 727: Pubchem_CID: None, SMILES: None\n",
      "Index 728: Pubchem_CID: 5317300, SMILES: CC(=CCC1=C(C=CC(=C1O)C2=COC3=C(C2=O)C=CC(=C3)O)O)C\n",
      "Index 729: Pubchem_CID: None, SMILES: None\n",
      "Index 730: Pubchem_CID: 5282767, SMILES: CCCCCCCCCCC=CCCCCCCCC(=O)O\n",
      "Index 731: Pubchem_CID: 5317478, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC=C(C2=O)C3=CC=C(C=C3)OC)O)C\n",
      "Index 732: Pubchem_CID: 5317479, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC=C(C2=O)C3=CC(=C(C=C3)OC)O)O)C\n",
      "Index 733: Pubchem_CID: 5317481, SMILES: CC(=CCC1=C2C(=C(C=C1O)O)C(=O)C(=CO2)C3=CC(=C(C=C3)OC)O)CO\n",
      "Index 734: Pubchem_CID: 5317482, SMILES: CC1(C=CC2=C(O1)C=C3C(=C2OC)C4=C(C5=C(O4)C=C(C=C5)O)C(=O)O3)C\n",
      "Index 735: Pubchem_CID: 480780, SMILES: CC(=CCC1=C(C=C2C(=C1O)C(=O)C(=CO2)C3=CC=C(C=C3)O)OC)C\n",
      "Index 736: Pubchem_CID: 5481949, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC=C(C2=O)C3=CC4=C(C(=C3)O)OC(C=C4)(C)C)O)C\n",
      "Index 737: Pubchem_CID: 480777, SMILES: CC(=CCC1=C(C=C2C(=C1OC)C=C(O2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 738: Pubchem_CID: 5481966, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=C(C2=O)O)C3=CC(=C(C=C3)O)O)O)C\n",
      "Index 739: Pubchem_CID: 5317483, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=C(C2=O)O)C3=CC(=C(C=C3)O)OC)O)C\n",
      "Index 740: Pubchem_CID: 480802, SMILES: CC(=CCC1=C(C=CC(=C1)C2=CC(=O)C3=C(O2)C=C(C(=C3O)CC=C(C)C)O)O)C\n",
      "Index 741: Pubchem_CID: 480815, SMILES: CC(=CCC1=C(C(=C(C=C1O)O)CC=C(C)C)CCC2=CC(=C(C=C2)O)O)C\n",
      "Index 742: Pubchem_CID: 480816, SMILES: CC(=CCC1=C(C(=C(C=C1CCC2=CC(=C(C=C2)O)O)O)CC=C(C)C)O)C\n",
      "Index 743: Pubchem_CID: 101606424, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(C(C2)O)(C)C)CCC3=CC(=C(C=C3)O)O)C\n",
      "Index 744: Pubchem_CID: 480818, SMILES: CC(=CCC1=C2CCC3=CC(=C(C=C3C2=C(C(=C1O)CC=C(C)C)O)O)O)C\n",
      "Index 745: Pubchem_CID: 480817, SMILES: CC(=CCC1=C2CCC3=CC(=C(C=C3C2=C(C=C1O)O)O)O)C\n",
      "Index 746: Pubchem_CID: 101666683, SMILES: CC1(C=CC2=C(O1)C=C3C(=C2)CC(CO3)C4=C(C=C(C=C4)OC)O)C\n",
      "Index 747: Pubchem_CID: 101666684, SMILES: CC(=CCC1=C(C=CC(=C1O)C2CC3=C(C=C(C=C3)O)OC2)OC)C\n",
      "Index 748: Pubchem_CID: None, SMILES: None\n",
      "Index 749: Pubchem_CID: None, SMILES: None\n",
      "Index 750: Pubchem_CID: 124049, SMILES: CC(=CCC1=C2C(=C(C=C1O)O)C(=O)CC(O2)C3=CC=CC=C3)C\n",
      "Index 751: Pubchem_CID: 480774, SMILES: CC1(C=CC2=C(C=CC(=C2O1)C3=CC4=C(C=C(C=C4)O)OC3)O)C\n",
      "Index 752: Pubchem_CID: 124052, SMILES: CC1(C=CC2=C(O1)C=CC3=C2OCC(C3)C4=C(C=C(C=C4)O)O)C\n",
      "Index 753: Pubchem_CID: 11596309, SMILES: CC(=CCC1=C(C=CC(=C1)C2CC(=O)C3=C(O2)C(=C(C=C3)O)CC=C(C)C)O)C\n",
      "Index 754: Pubchem_CID: 5317652, SMILES: CC1(C=CC2=C(O1)C=CC(=C2O)C3=COC4=C(C3=O)C=CC(=C4)O)C\n",
      "Index 755: Pubchem_CID: None, SMILES: None\n",
      "Index 756: Pubchem_CID: 480786, SMILES: CC(=CCC1=C(C=CC(=C1O)C2COC3=C(C2=O)C(=C(C(=C3)O)CC=C(C)C)O)O)C\n",
      "Index 757: Pubchem_CID: 132365, SMILES: CC1C(CC2(C1(C3CC(CC3(C2)O)(C)C)C)O)O\n",
      "Index 758: Pubchem_CID: 5481963, SMILES: CC(=CCC1=C(C=CC(=C1)C2=C(C(=O)C3=C(O2)C=C(C(=C3O)CC=C(C)C)O)O)O)C\n",
      "Index 759: Pubchem_CID: 480859, SMILES: CC(=CCC1=C(C2=C(C=C1O)OCC(C2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 760: Pubchem_CID: 480860, SMILES: CC(=CCC1=C(C=C2C(=C1OC)CC(CO2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 761: Pubchem_CID: 392442, SMILES: CC1(C=CC2=C(C=CC(=C2O1)C3COC4=CC(=CC(=C4C3=O)O)O)O)C\n",
      "Index 762: Pubchem_CID: None, SMILES: None\n",
      "Index 763: Pubchem_CID: None, SMILES: None\n",
      "Index 764: Pubchem_CID: None, SMILES: None\n",
      "Index 765: Pubchem_CID: 5317756, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=O)C(=C2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 766: Pubchem_CID: 442412, SMILES: CC1C(C(C(C(O1)OC2=CC(=CC(=C2C(=O)CCC3=CC=C(C=C3)O)O)O)O)O)O\n",
      "Index 767: Pubchem_CID: 480787, SMILES: CC(=CCC1=C(C=C2C(=C1OC)C=C(C(=O)O2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 768: Pubchem_CID: 5320083, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=O)C3=C2OC4=C3C=CC(=C4)O)OC)C\n",
      "Index 769: Pubchem_CID: 101939210, SMILES: COC1=CC=C(C=C1)C2=COC3=C(C2=O)C=CC(=C3)OC4C(C(C(C(O4)CO)O)O)OC5C(C(CO5)(CO)O)O\n",
      "Index 770: Pubchem_CID: 94320, SMILES: CC(=O)OC1CCC2(C(C1(C)C)CCC3(C2C(=O)C=C4C3(CCC5(C4CC(CC5)(C)C(=O)O)C)C)C)C\n",
      "Index 771: Pubchem_CID: 10114, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(CC5)(C)C(=O)O)C)C)C)C\n",
      "Index 772: Pubchem_CID: 12310283, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC(CC5)(C)CO)C)C)C)C\n",
      "Index 773: Pubchem_CID: 5317762, SMILES: CC1(C=CC2=C(O1)C(=CC(=C2)C3COC4=C(C3=O)C(=CC(=C4)O)OC)O)C\n",
      "Index 774: Pubchem_CID: 5317764, SMILES: CC(=CCC1=C(C(=CC(=C1)C2=COC3=CC(=CC(=C3C2=O)O)O)O)O)C\n",
      "Index 775: Pubchem_CID: 5317765, SMILES: CC1(C(CC2=C(O1)C=CC(=C2)C3=C(C(=O)C4=C(C=C(C=C4O3)O)O)O)O)C\n",
      "Index 776: Pubchem_CID: 5317765, SMILES: CC1(C(CC2=C(O1)C=CC(=C2)C3=C(C(=O)C4=C(C=C(C=C4O3)O)O)O)O)C\n",
      "Index 777: Pubchem_CID: 14982, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 778: Pubchem_CID: 14982, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 779: Pubchem_CID: 11546269, SMILES: CC(=CCC1=CC2=C(C=C1O)OC3=C2COC4=C3C(=C(C(=C4)O)CC=C(C)C)OC)C\n",
      "Index 780: Pubchem_CID: 11609971, SMILES: CC1(C=CC2=C(O1)C=CC3=C2OC4=C3COC5=C4C(=CC(=C5)O)OC)C\n",
      "Index 781: Pubchem_CID: 3083268, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 782: Pubchem_CID: 15742118, SMILES: CC(=CCC1=CC(=C(C=C1O)O)C(=O)CC(=O)C2=CC(=C(C=C2)O)CC=C(C)C)C\n",
      "Index 783: Pubchem_CID: 5317768, SMILES: COC1=CC=C(C=C1)C(=O)C=CC2=C(C=C(C=C2)O)OC\n",
      "Index 784: Pubchem_CID: 15818598, SMILES: CC(=CCC1=C(C2=C(C=C1O)OCC3=C2OC4=C3C=CC(=C4)O)OC)C\n",
      "Index 785: Pubchem_CID: 195396, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CCC5(C4CC6(CC5OC6=O)C)C)C(=O)OC)C)C\n",
      "Index 786: Pubchem_CID: 3083297, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)O)C)C=CC5=C6CC(C(CC6(CCC53C)C)OC7C(C(C(C(O7)C(=O)O)O)O)O)(C)C(=O)O)C)C\n",
      "Index 787: Pubchem_CID: 5317777, SMILES: C1OC2=C(O1)C(=C(C=C2)C3=COC4=C(C3=O)C=CC(=C4)O)O\n",
      "Index 788: Pubchem_CID: 156117, SMILES: CC1CC2C(CC(=O)C3=COC(=C23)C1=O)C(C)C\n",
      "Index 789: Pubchem_CID: 3559, SMILES: C1CN(CCC1(C2=CC=C(C=C2)Cl)O)CCCC(=O)C3=CC=C(C=C3)F\n",
      "Index 790: Pubchem_CID: 8900, SMILES: CCCCCCC\n",
      "Index 791: Pubchem_CID: 442774, SMILES: CC(=CCC1=C(C=CC(=C1O)C2CC3=C(C4=C(C=C3)OC(C=C4)(C)C)OC2)O)C\n",
      "Index 792: Pubchem_CID: 15228661, SMILES: CC1(C=CC2=C(O1)C=CC(=C2O)C3CC4=C(C5=C(C=C4)OC(C=C5)(C)C)OC3)C\n",
      "Index 793: Pubchem_CID: 3349565, SMILES: CCCCCCCCCCCCCCC=CCCCC(=O)O\n",
      "Index 794: Pubchem_CID: None, SMILES: None\n",
      "Index 795: Pubchem_CID: 21591148, SMILES: CC(=CCC1=C(C(=CC(=C1)C2=COC3=C(C2=O)C(=C(C(=C3)O)CC=C(C)C)O)O)O)C\n",
      "Index 796: Pubchem_CID: 5281255, SMILES: CC(=CCC1=C(C=CC(=C1O)C(=O)C=CC2=CC=C(C=C2)O)O)C\n",
      "Index 797: Pubchem_CID: 14187587, SMILES: CC1(CCC2=C(O1)C=C3C(=C2OC)C=C(C(=O)O3)C4=C(C=C(C=C4)O)O)C\n",
      "Index 798: Pubchem_CID: 124050, SMILES: CC1(CCC2=C(O1)C=C3C(=C2OC)C4=C(C5=C(O4)C=C(C=C5)O)C(=O)O3)C\n",
      "Index 799: Pubchem_CID: 148619, SMILES: CC(=C)C(COC1=C2C(=CC3=C1OC=C3)C=CC(=O)O2)O\n",
      "Index 800: Pubchem_CID: None, SMILES: None\n",
      "Index 801: Pubchem_CID: 5318585, SMILES: CC(=CCC1=C(C=CC(=C1)C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O)O)C\n",
      "Index 802: Pubchem_CID: 5274591, SMILES: CN1CCC2=CC(=C(C=C2C1CC3=CC=C(C=C3)OC)OC4=C(C=CC(=C4)CC5C6=CC(=C(C=C6CCN5C)OC)O)O)OC\n",
      "Index 803: Pubchem_CID: 638278, SMILES: C1=CC(=CC=C1C=CC(=O)C2=C(C=C(C=C2)O)O)O\n",
      "Index 804: Pubchem_CID: 5318591, SMILES: C1=CC(=CC=C1C=CC(=O)C2=C(C=C(C=C2)O)O)OC3C(C(C(C(O3)CO)O)O)O\n",
      "Index 805: Pubchem_CID: 6442433, SMILES: C1C(C(C(O1)OC2C(C(C(OC2OC3=CC=C(C=C3)C=CC(=O)C4=C(C=C(C=C4)O)O)CO)O)O)O)(CO)O\n",
      "Index 806: Pubchem_CID: None, SMILES: None\n",
      "Index 807: Pubchem_CID: None, SMILES: None\n",
      "Index 808: Pubchem_CID: 5318619, SMILES: COC1=CC2=C(C=C1)C(=O)C(=CO2)C3=CC=C(C=C3)OC4C(C(C(C(O4)CO)O)O)O\n",
      "Index 809: Pubchem_CID: None, SMILES: None\n",
      "Index 810: Pubchem_CID: 3084995, SMILES: C1C(C(C(C(O1)C2=C(C(=C3C(=C2O)C(=O)C=C(O3)C4=CC=C(C=C4)O)C5C(C(C(C(O5)CO)O)O)O)O)O)O)O\n",
      "Index 811: Pubchem_CID: 5318679, SMILES: COC1=CC(=CC2=C1C3=C(C4=C(O3)C=C(C=C4)O)C(=O)O2)O\n",
      "Index 812: Pubchem_CID: 12310578, SMILES: CN1CCC2=CC(=C3C4=C2C1CC5=CC=C(C=C5)OC6=C(C=CC(=C6)CC7C8=CC(=C(O3)C=C8CCN7C)O4)OC)OC\n",
      "Index 813: Pubchem_CID: 101422758, SMILES: CC1C(C(C(C(O1)C2=C(C(=C3C(=C2O)C(=O)C=C(O3)C4=CC=C(C=C4)O)C5C(C(C(C(O5)CO)O)O)O)O)O)O)O\n",
      "Index 814: Pubchem_CID: 15516846, SMILES: CC(=CCC1=CC2=C(C=C1O)OC(=CC2=O)C3=CC4=C(C=C3)OC(C=C4)(C)C)C\n",
      "Index 815: Pubchem_CID: 101666840, SMILES: CC(=CCC1=C(C2=C(C=C1O)OCC3C2OC4=C3C=C5C=CC(OC5=C4)(C)C)OC)C\n",
      "Index 816: Pubchem_CID: 480863, SMILES: CC(=CCC1=C(C=CC(=C1O)C2CC3=C(C=C4C(=C3OC)CCC(O4)(C)C)OC2)O)C\n",
      "Index 817: Pubchem_CID: 131753032, SMILES: CC(=CCC1=C(C(=C2C(=C1O)C(=O)C(=CO2)C3=C4C(=C(C=C3)O)C=CC(O4)(C)C)CC=C(C)C)O)C\n",
      "Index 818: Pubchem_CID: 10319154, SMILES: CC(=CCC1=C(C=CC(=C1)C2C(C(=O)C3=C(O2)C4=C(C=C3)OC(C=C4)(C)C)O)O)C\n",
      "Index 819: Pubchem_CID: None, SMILES: None\n",
      "Index 820: Pubchem_CID: None, SMILES: None\n",
      "Index 821: Pubchem_CID: None, SMILES: None\n",
      "Index 822: Pubchem_CID: None, SMILES: None\n",
      "Index 823: Pubchem_CID: None, SMILES: None\n",
      "Index 824: Pubchem_CID: 8433, SMILES: C1=CC=C(C=C1)C(=O)CC(=O)C2=CC=CC=C2\n",
      "Index 825: Pubchem_CID: None, SMILES: None\n",
      "Index 826: Pubchem_CID: 15840593, SMILES: CC(=CCC1=C(C=CC2=C1OCC3C2OC4=C3C=CC(=C4)OC)O)C\n",
      "Index 827: Pubchem_CID: 11099375, SMILES: CC(=CCC1=C(C=CC(=C1)C=CC(=O)C2=C(C=C(C=C2)O)O)O)C\n",
      "Index 828: Pubchem_CID: 636883, SMILES: CC(=C)C1CC2=C(O1)C=C3C(=C2O)C(=O)C(=CO3)C4=CC=C(C=C4)O\n",
      "Index 829: Pubchem_CID: None, SMILES: None\n",
      "Index 830: Pubchem_CID: None, SMILES: None\n",
      "Index 831: Pubchem_CID: 11827914, SMILES: COC1=C(C=CC(=C1O)O)C2C(C(=CC3=CC(=C(C(=C23)OC)O)O)C(=O)C4=CC=C(C=C4)O)C(=O)C5=CC=C(C=C5)O\n",
      "Index 832: Pubchem_CID: 5318998, SMILES: CC(C)(C=C)C1=C(C=C(C(=C1)C=CC(=O)C2=CC=C(C=C2)O)OC)O\n",
      "Index 833: Pubchem_CID: 5318999, SMILES: COC1=C(C=CC(=C1O)O)C=CC(=O)C2=CC=C(C=C2)O\n",
      "Index 834: Pubchem_CID: 49856081, SMILES: CC(C)(C=C)C1=C(C=C(C(=C1)C=CC(=O)C2=C(C=C(C=C2)O)O)OC)O\n",
      "Index 835: Pubchem_CID: None, SMILES: None\n",
      "Index 836: Pubchem_CID: 503731, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=C2)C3=C(C=C(C=C3)O)O)OC)C\n",
      "Index 837: Pubchem_CID: 5481964, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=C(C2=O)O)C3=CC=C(C=C3)O)O)C\n",
      "Index 838: Pubchem_CID: 5319001, SMILES: CC(C)(C1CC2=C(O1)C=C3C(=C2OC)C=C(C(=O)O3)C4=C(C=C(C=C4)O)O)O\n",
      "Index 839: Pubchem_CID: 392443, SMILES: CC1(C=CC2=C(O1)C=CC(=C2O)C3COC4=CC(=CC(=C4C3=O)O)O)C\n",
      "Index 840: Pubchem_CID: None, SMILES: None\n",
      "Index 841: Pubchem_CID: 5481234, SMILES: CC1(C=CC2=C(O1)C=CC(=C2O)C3=COC4=CC(=CC(=C4C3=O)O)O)C\n",
      "Index 842: Pubchem_CID: 11111496, SMILES: CC(=CCC1=C2C(=C(C=C1O)O)C(=O)CC(O2)C3=CC(=C(C=C3)O)O)CO\n",
      "Index 843: Pubchem_CID: 122851, SMILES: CC1(CCC2=C(O1)C=C3C(=C2OC)C=C(C(=O)O3)C4=C(C=C(C=C4)O)O)CO\n",
      "Index 844: Pubchem_CID: 101938903, SMILES: COC1=C(C=CC(=C1)C=CC(=O)OCC2(COC(C2O)OC3C(C(C(OC3OC4=CC=C(C=C4)C=CC(=O)C5=C(C=C(C=C5)O)O)CO)O)O)O)O\n",
      "Index 845: Pubchem_CID: 42607811, SMILES: C1C(OC2=C(C1=O)C=CC(=C2)O)C3=CC=C(C=C3)OC4C(C(C(C(O4)CO)O)O)OC5C(C(CO5)(COC(=O)C6=CNC7=CC=CC=C76)O)O\n",
      "Index 846: Pubchem_CID: 13457500, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)CC=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 847: Pubchem_CID: 13457500, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)CC=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 848: Pubchem_CID: 101589043, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)C=CC6=C7CC(CCC7(CCC63C)C)(C)C(=O)O)C)C\n",
      "Index 849: Pubchem_CID: None, SMILES: None\n",
      "Index 850: Pubchem_CID: 131752373, SMILES: CC1C(C(C(C(O1)OC2C(C(OC(C2O)OC3C(C(C(OC3OC4CCC5(C(C4(C)C)CCC6(C5CC=C7C6(CCC8(C7CC(CC8OC(=O)C)(C)C(=O)O)C)C)C)C)C(=O)O)O)O)C(=O)O)O)O)O)O\n",
      "Index 851: Pubchem_CID: 86278258, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC8(CC7OC8=O)C)C)C)C)C\n",
      "Index 852: Pubchem_CID: 73795903, SMILES: CC1C(C(C(C(O1)OC2C(C(C(OC2OC3C(C(C(OC3OC4CCC5(C(C4(C)C)CCC6(C5CC=C7C6(CCC8(C7CC9(CC8OC9=O)C)C)C)C)C)C(=O)O)O)O)C(=O)O)O)O)O)O)O\n",
      "Index 853: Pubchem_CID: None, SMILES: None\n",
      "Index 854: Pubchem_CID: None, SMILES: None\n",
      "Index 855: Pubchem_CID: 14891565, SMILES: CC12CCC(CC1C3=CC(=O)C4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)OC6C(C(C(C(O6)C(=O)O)O)O)OC7C(C(C(C(O7)C(=O)O)O)O)O)C)(C)C(=O)O\n",
      "Index 856: Pubchem_CID: 14891565, SMILES: CC12CCC(CC1C3=CC(=O)C4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)OC6C(C(C(C(O6)C(=O)O)O)O)OC7C(C(C(C(O7)C(=O)O)O)O)O)C)(C)C(=O)O\n",
      "Index 857: Pubchem_CID: None, SMILES: None\n",
      "Index 858: Pubchem_CID: 12889143, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C\n",
      "Index 859: Pubchem_CID: None, SMILES: None\n",
      "Index 860: Pubchem_CID: 14891570, SMILES: CC12CCC(CC1C3=CCC4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)OC6C(C(C(C(O6)C(=O)O)O)O)OC7C(C(C(C(O7)C(=O)O)O)O)O)C)(C)C(=O)O\n",
      "Index 861: Pubchem_CID: 14891570, SMILES: CC12CCC(CC1C3=CCC4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)OC6C(C(C(C(O6)C(=O)O)O)O)OC7C(C(C(C(O7)C(=O)O)O)O)O)C)(C)C(=O)O\n",
      "Index 862: Pubchem_CID: None, SMILES: None\n",
      "Index 863: Pubchem_CID: 101589724, SMILES: CC12CCC(CC1=C3C=CC4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)OC6C(C(C(C(O6)C(=O)O)O)O)OC7C(C(C(C(O7)C(=O)O)O)O)O)C)(C)C(=O)O\n",
      "Index 864: Pubchem_CID: 101589724, SMILES: CC12CCC(CC1=C3C=CC4C5(CCC(C(C5CCC4(C3(CC2)C)C)(C)CO)OC6C(C(C(C(O6)C(=O)O)O)O)OC7C(C(C(C(O7)C(=O)O)O)O)O)C)(C)C(=O)O\n",
      "Index 865: Pubchem_CID: None, SMILES: None\n",
      "Index 866: Pubchem_CID: None, SMILES: None\n",
      "Index 867: Pubchem_CID: None, SMILES: None\n",
      "Index 868: Pubchem_CID: None, SMILES: None\n",
      "Index 869: Pubchem_CID: None, SMILES: None\n",
      "Index 870: Pubchem_CID: None, SMILES: None\n",
      "Index 871: Pubchem_CID: None, SMILES: None\n",
      "Index 872: Pubchem_CID: None, SMILES: None\n",
      "Index 873: Pubchem_CID: None, SMILES: None\n",
      "Index 874: Pubchem_CID: None, SMILES: None\n",
      "Index 875: Pubchem_CID: 480865, SMILES: CC(=CCC1=C(C=CC(=C1O)C2CC3=C(C=C(C(=C3OC)CC=C(C)C)O)OC2)O)C\n",
      "Index 876: Pubchem_CID: 5319013, SMILES: CC(=CCC1=C(C=C(C(=C1OC)C2=COC3=C(C2=O)C=CC(=C3)O)O)OC)C\n",
      "Index 877: Pubchem_CID: 196831, SMILES: CC(=CCC1=C(C=CC(=C1O)C2CC3=C(C(=C(C=C3OC2)OC)CC=C(C)C)OC)O)C\n",
      "Index 878: Pubchem_CID: 196831, SMILES: CC(=CCC1=C(C=CC(=C1O)C2CC3=C(C(=C(C=C3OC2)OC)CC=C(C)C)OC)O)C\n",
      "Index 879: Pubchem_CID: 14282455, SMILES: C1C(C(C(O1)OC2C(C(C(OC2OC3=CC(=C(C=C3)C(=O)C=CC4=CC=C(C=C4)O)O)CO)O)O)O)(CO)O\n",
      "Index 880: Pubchem_CID: None, SMILES: None\n",
      "Index 881: Pubchem_CID: None, SMILES: None\n",
      "Index 882: Pubchem_CID: None, SMILES: None\n",
      "Index 883: Pubchem_CID: 10076238, SMILES: C1C(OC2=C(C1=O)C=CC(=C2)O)C3=CC=C(C=C3)OC4C(C(C(C(O4)CO)O)O)OC5C(C(CO5)(CO)O)O\n",
      "Index 884: Pubchem_CID: 131751571, SMILES: CC1(C2CCC3(C(C2(CCC1O)C)C(=O)C=C4C3(CC5C6(C4CC(C(C6)O5)(C)C(=O)O)C)C)C)C\n",
      "Index 885: Pubchem_CID: 91510, SMILES: C1C2C(C3=C(O1)C=C(C=C3)O)OC4=CC5=C(C=C24)OCO5\n",
      "Index 886: Pubchem_CID: 147515, SMILES: CCCC(CCC)C(=O)[O-].CCCC(CCC)C(=O)[O-].[Mg+2]\n",
      "Index 887: Pubchem_CID: 441347, SMILES: CCN(CC)CCNC(=O)C1=CC(=C(C=C1OC)N)Cl.O.Cl\n",
      "Index 888: Pubchem_CID: None, SMILES: None\n",
      "Index 889: Pubchem_CID: None, SMILES: None\n",
      "Index 890: Pubchem_CID: None, SMILES: None\n",
      "Index 891: Pubchem_CID: None, SMILES: None\n",
      "Index 892: Pubchem_CID: None, SMILES: None\n",
      "Index 893: Pubchem_CID: None, SMILES: None\n",
      "Index 894: Pubchem_CID: None, SMILES: None\n",
      "Index 895: Pubchem_CID: 7296, SMILES: CC1CCCC1\n",
      "Index 896: Pubchem_CID: None, SMILES: None\n",
      "Index 897: Pubchem_CID: 880, SMILES: CC(=O)C=O\n",
      "Index 898: Pubchem_CID: 4168, SMILES: CCN(CC)CCNC(=O)C1=CC(=C(C=C1OC)N)Cl\n",
      "Index 899: Pubchem_CID: 21761, SMILES: CCN(CC)CCNC(=O)C1=CC(=C(C=C1OC)N)Cl.Cl.Cl\n",
      "Index 900: Pubchem_CID: 23659, SMILES: CCN(CC)CCNC(=O)C1=CC(=C(C=C1OC)N)Cl.Cl\n",
      "Index 901: Pubchem_CID: 55245, SMILES: CC#CC1(CCC2C1(CC(C3=C4CCC(=O)C=C4CCC23)C5=CC=C(C=C5)N(C)C)C)O\n",
      "Index 902: Pubchem_CID: 5481663, SMILES: CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=C(OC4=CC(=CC(=C4C3=O)O)O)C5=CC(=C(C=C5)O)OC)O)O)O)O)O)O\n",
      "Index 903: Pubchem_CID: 10356588, SMILES: CN1CCC23C=CC(=O)CC2OC4=C(C=CC(=C34)C1)OC\n",
      "Index 904: Pubchem_CID: 5320083, SMILES: CC(=CCC1=C(C2=C(C=C1O)OC(=O)C3=C2OC4=C3C=CC(=C4)O)OC)C\n",
      "Index 905: Pubchem_CID: 131802, SMILES: CC(=CCCC(C)(C=C)OC1C(C(C(C(O1)COC2C(C(C(CO2)O)O)O)O)O)O)C\n",
      "Index 906: Pubchem_CID: None, SMILES: None\n",
      "Index 907: Pubchem_CID: 6553885, SMILES: CC1CCC(C(C1)O)C(=C)C\n",
      "Index 908: Pubchem_CID: 5280805, SMILES: CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=C(OC4=CC(=CC(=C4C3=O)O)O)C5=CC(=C(C=C5)O)O)O)O)O)O)O)O\n",
      "Index 909: Pubchem_CID: 51666248, SMILES: C1C(OC2=C(C1=O)C=CC(=C2)OC3C(C(C(C(O3)CO)O)O)O)C4=CC=C(C=C4)O\n",
      "Index 910: Pubchem_CID: 442429, SMILES: CC1CCC2C1C(OCC2C)O\n",
      "Index 911: Pubchem_CID: 5320118, SMILES: CC(=CCC1=C(C=CC(=C1O)O)C2=C(C(=O)C3=CC(=C(C=C3O2)O)O)O)C\n",
      "Index 912: Pubchem_CID: None, SMILES: None\n",
      "Index 913: Pubchem_CID: 5318767, SMILES: CC1C(C(C(C(O1)OCC2C(C(C(C(O2)OC3=C(OC4=CC(=CC(=C4C3=O)O)O)C5=CC=C(C=C5)O)O)O)O)O)O)O\n",
      "Index 914: Pubchem_CID: 96506, SMILES: C1=CC(=CC=C1C2=CC(=O)C3=C(C(=C(C(=C3O2)O)O)O)O)O\n",
      "Index 915: Pubchem_CID: 517653, SMILES: CCCCC=CC=C\n",
      "Index 916: Pubchem_CID: 73442, SMILES: COC1=C(C2=C[N+]3=C(C=C2C=C1)C4=CC(=C(C=C4CC3)OC)OC)OC.[Cl-]\n",
      "Index 917: Pubchem_CID: 200119, SMILES: COC1=C(C2=C[N+]3=C(C=C2C=C1)C4=CC(=C(C=C4CC3)OC)OC)OC.[OH-]\n",
      "Index 918: Pubchem_CID: 44257530, SMILES: CC(=CCC1=C(C=CC2=C1OC(=O)C3=C2OC4=C3C=CC(=C4)O)O)C\n",
      "Index 919: Pubchem_CID: 162412, SMILES: CC1(C=CC2=C(O1)C=CC(=C2O)C3CC4=C(C=C(C=C4)O)OC3)C\n",
      "Index 920: Pubchem_CID: 188300, SMILES: CC(=C)C1C(O1)C2=C(C=CC3=C2OC(=O)C=C3)OC\n",
      "Index 921: Pubchem_CID: 5281804, SMILES: COC1=CC(=C2C(=C1)OC=C(C2=O)C3=CC=C(C=C3)O)O\n",
      "Index 922: Pubchem_CID: 49818210, SMILES: CCCCCCC(=O)CCCCCCCC1CCC(=O)O1\n",
      "Index 923: Pubchem_CID: 101650325, SMILES: CC1C(C(C(C(O1)OC2CCC3(C(C2)CCC4C3CCC5(C4(CCC5C6=CC(=O)OC6)O)C)CO)O)OC)O\n",
      "Index 924: Pubchem_CID: 442658, SMILES: C1C(C(C(C(O1)C2=C3C(=C(C(=C2O)C4C(C(C(C(O4)CO)O)O)O)O)C(=O)C=C(O3)C5=CC=C(C=C5)O)O)O)O\n",
      "Index 925: Pubchem_CID: 5481948, SMILES: CC1(C=CC2=C(O1)C(=CC(=C2)C3=COC4=CC(=CC(=C4C3=O)O)O)O)C\n",
      "Index 926: Pubchem_CID: 90488848, SMILES: CC(C)CC(C(=O)NC(CCC(=O)O)C(=O)NC(CC1=CC=CC=C1)C(=O)O)NC(=O)C2CCCN2C(=O)C(CC3=CC=CC=C3)NC(=O)C(C)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)C(CO)NC(=O)C(CCC(=O)O)NC(=O)C(CC(=O)O)NC(=O)C(CCC(=O)O)NC(=O)C(C)NC(=O)CNC(=O)C(CC(=O)N)NC(=O)C4CCCN4C(=O)C(CC5=CC=C(C=C5)O)NC(=O)C(C(C)C)NC(=O)C(CCCCN)NC(=O)C(C(C)C)NC(=O)C6CCCN6C(=O)C(CCCNC(=N)N)NC(=O)C(CCCNC(=N)N)NC(=O)C(CCCCN)NC(=O)C(CCCCN)NC(=O)CNC(=O)C(C(C)C)NC(=O)C7CCCN7C(=O)C(CCCCN)NC(=O)CNC(=O)C(CC8=CNC9=CC=CC=C98)NC(=O)C(CCCNC(=N)N)NC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC1=CNC=N1)NC(=O)C(CCC(=O)O)NC(=O)C(CCSC)NC(=O)C(CO)NC(=O)C(CC1=CC=C(C=C1)O)NC(=O)C(CO)N\n",
      "Index 927: Pubchem_CID: 10336244, SMILES: CC1(C=CC2=C(O1)C=CC3=C2OCC4C3OC5=C4C=CC(=C5)O)C\n",
      "Index 928: Pubchem_CID: 73205, SMILES: CC(=CCC1=C(C(=CC(=C1)C2CC(=O)C3=C(C=C(C=C3O2)O)O)O)O)C\n",
      "Index 929: Pubchem_CID: 16760703, SMILES: CCCC(CCC)C(=O)[O-].[Na+]\n",
      "Index 930: Pubchem_CID: 5394, SMILES: CN1C(=O)N2C=NC(=C2N=N1)C(=O)N\n",
      "Index 931: Pubchem_CID: 159809, SMILES: CC1C2=C(CCN1)C3=C(N2)C=C(C=C3)OC\n",
      "Index 932: Pubchem_CID: 5282149, SMILES: C1=CC(=CC=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)OC4C(C(C(C(O4)CO)O)O)O)O\n",
      "Index 933: Pubchem_CID: 656656, SMILES: CC1(C2CCC3(C(C2(CCC1OC4C(C(C(C(O4)C(=O)O)O)O)OC5C(C(C(C(O5)C(=O)O)O)O)O)C)C(=O)C=C6C3(CCC7(C6CC(CC7)(C)C(=O)O)C)C)C)C.N.N\n",
      "Index 934: Pubchem_CID: 192490, SMILES: CC(=CCC1=CC(=C(C=C1C2=C(C(=O)C3=C(O2)C=CC(=C3O)O)OC)O)O)C\n",
      "Index 935: Pubchem_CID: 5315125, SMILES: CC(=CCC1=C(C(=CC(=C1)C2=CC(=O)C3=C(C=C(C=C3O2)O)O)O)O)C\n",
      "Index 936: Pubchem_CID: 5315126, SMILES: CC(=CCC1=C(C(=CC(=C1)C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O)O)O)C\n",
      "Index 937: Pubchem_CID: 5315127, SMILES: CC(=CCC1=C(C(=CC(=C1)C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)OC)O)O)C\n",
      "Index 938: Pubchem_CID: None, SMILES: None\n",
      "Index 939: Pubchem_CID: 11783899, SMILES: CC(=CCC1=C(C=C(C=C1OCC=C(C)C)O)CCC2=CC(=C(C=C2)O)O)C\n",
      "Index 940: Pubchem_CID: 1176, SMILES: C(=O)(N)N\n",
      "Index 941: Pubchem_CID: 64945, SMILES: CC1CCC2(CCC3(C(=CCC4C3(CCC5C4(CCC(C5(C)C)O)C)C)C2C1C)C)C(=O)O\n",
      "Index 942: Pubchem_CID: None, SMILES: None\n",
      "Index 943: Pubchem_CID: 3121, SMILES: CCCC(CCC)C(=O)O\n",
      "Index 944: Pubchem_CID: 656493, SMILES: C1C(C(C(C(O1)OCC2C(C(C(C(O2)OC(C#N)C3=CC=CC=C3)O)O)O)O)O)O\n",
      "Index 945: Pubchem_CID: 54678486, SMILES: CC(=O)CC(C1=CC=CC=C1)C2=C(C3=CC=CC=C3OC2=O)O\n",
      "Index 946: Pubchem_CID: 23706212, SMILES: CC(=O)CC(C1=CC=CC=C1)C2=C(C3=CC=CC=C3OC2=O)[O-].[K+]\n",
      "Index 947: Pubchem_CID: 16204922, SMILES: CC(=O)CC(C1=CC=CC=C1)C2=C(C3=CC=CC=C3OC2=O)[O-].[Na+]\n",
      "Index 948: Pubchem_CID: 14769500, SMILES: CC1(C=CC2=C(O1)C=CC(=C2)C3CC(=O)C4=C(O3)C5=C(C=C4)OC(C=C5)(C)C)C\n",
      "Index 949: Pubchem_CID: 5315396, SMILES: CC(=CCC1=C(C=CC(=C1)C2=CC(=O)C3=C(C=C(C=C3O2)O)O)O)C\n",
      "Index 950: Pubchem_CID: 5994, SMILES: CC(=O)C1CCC2C1(CCC3C2CCC4=CC(=O)CCC34C)C\n",
      "Index 951: Pubchem_CID: None, SMILES: None\n",
      "数据已保存到文件：01.Drug_Ingredients\\03.ingredients_augmenteddata\\ETCM_augmented.csv\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def get_cid_and_smiles(compound_name, aliases, molecular_formula):\n",
    "    try:\n",
    "        # 首先使用化合物名称和分子式进行查询\n",
    "        results = pcp.get_compounds(compound_name, namespace='name')\n",
    "        results = [compound for compound in results if compound.molecular_formula == molecular_formula]\n",
    "\n",
    "        if not results:\n",
    "            # 如果没有结果，再使用别名进行查询\n",
    "            for alias in aliases:\n",
    "                results = pcp.get_compounds(alias, namespace='name')\n",
    "                results = [compound for compound in results if compound.molecular_formula == molecular_formula]\n",
    "                if results:\n",
    "                    break\n",
    "\n",
    "        if results:\n",
    "            cid = results[0].cid\n",
    "            canonical_smiles = results[0].canonical_smiles\n",
    "            return cid, canonical_smiles\n",
    "        else:\n",
    "            return None, None\n",
    "    except pcp.BadRequestError:\n",
    "        print(f\"BadRequestError: 检索 {compound_name} 和 {molecular_formula} 时出现错误\")\n",
    "        return None, None\n",
    "    except Exception as e:\n",
    "        print(f\"检索 {compound_name} 和 {molecular_formula} 时出现错误: {e}\")\n",
    "        return None, None\n",
    "\n",
    "\n",
    "def process_compound(index, row):\n",
    "    compound_name = row['component_name_en']\n",
    "    aliases = row['alias'].split('|') if isinstance(row['alias'], str) else []\n",
    "    molecular_formula = row['molecular_formula']\n",
    "\n",
    "    cid, smiles = get_cid_and_smiles(compound_name, aliases, molecular_formula)\n",
    "\n",
    "    return index, cid, smiles\n",
    "\n",
    "# 之后的代码保持不变\n",
    "def get_file_encoding(file_path):\n",
    "    with open(file_path, 'rb') as f:\n",
    "        result = chardet.detect(f.read())\n",
    "    return result['encoding']\n",
    "\n",
    "\n",
    "file_name = r\"01.Drug_Ingredients\\02.ingredients_preprocesseddata\\03.ETCM.csv\"\n",
    "\n",
    "file_encoding = get_file_encoding(file_name)\n",
    "df = pd.read_csv(file_name, encoding=file_encoding)\n",
    "\n",
    "df['Pubchem_CID'] = ''\n",
    "df['SMILES'] = ''\n",
    "\n",
    "# 设置线程数，根据您的系统性能进行调整\n",
    "num_threads = 20\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=num_threads) as executor:\n",
    "    results = list(executor.map(process_compound, df.index, (row for _, row in df.iterrows())))\n",
    "\n",
    "for result in results:\n",
    "    index, cid, smiles = result\n",
    "    df.at[index, 'Pubchem_CID'] = cid\n",
    "    df.at[index, 'SMILES'] = smiles\n",
    "    print(f\"Index {index}: Pubchem_CID: {cid}, SMILES: {smiles}\")\n",
    "\n",
    "output_file_name = r\"01.Drug_Ingredients\\03.ingredients_augmenteddata\\ETCM_augmented.csv\"\n",
    "df.to_csv(output_file_name, index=False, encoding=file_encoding)\n",
    "print(f\"数据已保存到文件：{output_file_name}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HIT数据库_直接复制\n",
    "\n",
    "分子都是基于证据，且都具有pubchemID和smiles值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义源文件路径\n",
    "source_path = \"01.Drug_Ingredients/02.ingredients_preprocesseddata/01.HIT.csv\"\n",
    "\n",
    "# 定义目标文件路径\n",
    "target_path = \"01.Drug_Ingredients/03.ingredients_augmenteddata/HIT.csv\"\n",
    "\n",
    "# 复制文件\n",
    "shutil.copy2(source_path, target_path)\n",
    "\n",
    "# 重命名文件\n",
    "os.rename(target_path, os.path.join(os.path.dirname(target_path), \"HIT.csv\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 数据初筛"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HERB数据库_删除无Pubchem ID的行\n",
    "\n",
    "有OB, DL, Pubchem ID, SMELIS。若无Pubchem ID的值同样没有SMELIS，并且数据库不提供分子结构图，故删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取原始数据\n",
    "df = pd.read_csv(\"01.Drug_Ingredients/03.ingredients_augmenteddata/HERB_augmented.csv\")\n",
    "\n",
    "# 删除PubChem_id列的值为空的行\n",
    "df = df.dropna(subset=['PubChem_id'])\n",
    "\n",
    "# 将处理后的数据输出到新的csv文件中\n",
    "\n",
    "os.makedirs(\"01.Drug_Ingredients/04.ingredients_filtereddata\", exist_ok=True)\n",
    "\n",
    "df.to_csv(\"01.Drug_Ingredients/04.ingredients_filtereddata/HERB_filtered.csv\", index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETCM数据库和HIT数据库_直接复制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 源文件路径\n",
    "hit_file = \"01.Drug_Ingredients/03.ingredients_augmenteddata/HIT.csv\"\n",
    "etcm_file = \"01.Drug_Ingredients/03.ingredients_preprocesseddata/ETCM_augmented.csv\"\n",
    "\n",
    "# 目标文件夹路径\n",
    "target_folder = \"01.Drug_Ingredients/04.ingredients_filtereddata\"\n",
    "\n",
    "# 将HIT.csv复制到目标文件夹\n",
    "shutil.copy2(hit_file, target_folder)\n",
    "\n",
    "# 将ETCM.csv复制到目标文件夹\n",
    "shutil.copy2(etcm_file, target_folder)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 ADMETlab2.0\n",
    "\n",
    "https://admetmesh.scbdd.com/"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 二次筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.7 多数据库文件合并"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多数据库文件合并，veen图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = r\"\"\n",
    "output_file = r\"\"\n",
    "\n",
    "def merge_xlsx_files(input_folder, output_file):\n",
    "    xlsx_files = [file for file in glob.glob(os.path.join(input_folder, \"*.xlsx\")) if not file.startswith('~$')]\n",
    "    print(f\"Found {len(xlsx_files)} xlsx files in the input folder.\")\n",
    "\n",
    "    merged_data = pd.DataFrame()\n",
    "\n",
    "    for xlsx_file in xlsx_files:\n",
    "        data = pd.read_excel(xlsx_file)\n",
    "        source = os.path.basename(xlsx_file).split('_')[0]\n",
    "        data.insert(0, \"Source\", source)\n",
    "        merged_data = pd.concat([merged_data, data], ignore_index=True)\n",
    "\n",
    "    merged_data.to_excel(output_file, index=False)\n",
    "    print(f\"Merged data saved to {output_file}\")\n",
    "\n",
    "merge_xlsx_files(input_folder, output_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 形成ingredients_smiles唯一值csv文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file = r\"\"\n",
    "output_file = r\"\"\n",
    "\n",
    "def remove_duplicates(input_file, output_file):\n",
    "    data = pd.read_excel(input_file)\n",
    "\n",
    "    # 删除Pubchem_ID列的值重复的行，仅保留第一个\n",
    "    data = data.drop_duplicates(subset=[\"Pubchem_CID\"], keep=\"first\")\n",
    "\n",
    "    # 删除同时具有相同 \"Ingredient name\" 和 \"molecular_formula\" 值的行，仅保留第一个\n",
    "    data = data.drop_duplicates(subset=[\"Ingredient name\", \"molecular_formula\"], keep=\"first\")\n",
    "\n",
    "    # 保存结果到输出文件\n",
    "    data.to_excel(output_file, index=False)\n",
    "\n",
    "remove_duplicates(input_file, output_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 形成中药分类下的成分唯一值，中药对应成分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "input_file = \"\"\n",
    "output_file = \"\"\n",
    "\n",
    "def remove_duplicates(input_file, output_file):\n",
    "    data = pd.read_excel(input_file)\n",
    "    # 按照source列的值分类\n",
    "    grouped = data.groupby(\"source\")\n",
    "    # 在每个source小类别里，删除Pubchem_ID列的值重复的行，仅保留第一个\n",
    "    for name, group in grouped:\n",
    "        group = group.drop_duplicates(subset=[\"Pubchem_ID\"], keep=\"first\")\n",
    "        # 删除同时具有相同 \"Ingredient name\" 和 \"molecular_formula\" 值的行，仅保留第一个\n",
    "        group = group.drop_duplicates(subset=[\"Ingredient name\", \"molecular_formula\"], keep=\"first\")\n",
    "        # 将处理后的数据添加到结果中\n",
    "        if name == grouped.groups.keys()[0]:\n",
    "            result = group\n",
    "        else:\n",
    "            result = pd.concat([result, group])\n",
    "    # 保存结果到输出文件\n",
    "    result.to_excel(output_file, index=False)\n",
    "\n",
    "remove_duplicates(input_file, output_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据名称和分子式补充CID和SMILES"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 合并去重"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 去重-得到全部无重复成分"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 去重—各中药内保留成分唯一值"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 INGREDIENT-TARGETS"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 靶点预测"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 获取靶点：cmd运行代码 python targets.py --input \"C:\\Users\\cdzyy\\OneDrive - stu.cdutcm.edu.cn\\Paper writing\\Network pharmacology\\Haoqin-qingdan\\01_drug_ingredients\\05.merge\\no_duplicates\\Ingredient_smiles.csv\" --output \"C:\\Users\\cdzyy\\OneDrive - stu.cdutcm.edu.cn\\Paper writing\\Network pharmacology\\Haoqin-qingdan\\01_drug_ingredients\\file.csv\"\n",
    "2. uniprot转换entry name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python 01_drug_ingredients/targets.py --input \"C:\\Users\\cdzyy\\OneDrive - stu.cdutcm.edu.cn\\Paper writing\\Network pharmacology\\Haoqin-qingdan\\01_drug_ingredients\\05.merge\\SMILES_qed0.67.csv\" --output \"C:\\Users\\cdzyy\\OneDrive - stu.cdutcm.edu.cn\\Paper writing\\Network pharmacology\\Haoqin-qingdan\\01_drug_ingredients\\05.merge\\TARGETS_qed0.67.csv\"\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 初筛_保留human"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '02_ingredients_targets/021_ingredients_targets_orig'\n",
    "output_folder = '02_ingredients_targets/022_ingredients_targets_processed'\n",
    "\n",
    "# 创建输出文件夹（如果不存在）\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 获取文件夹中的所有 CSV 文件\n",
    "csv_files = [f for f in os.listdir(input_folder) if f.endswith('.csv')]\n",
    "\n",
    "for csv_file in csv_files:\n",
    "    file_path = os.path.join(input_folder, csv_file)\n",
    "\n",
    "    # 使用 chardet 自动检测编码格式\n",
    "    with open(file_path, 'rb') as f:\n",
    "        result = chardet.detect(f.read())\n",
    "\n",
    "    # 读取 CSV 文件\n",
    "    df = pd.read_csv(file_path, index_col=None, header=0, encoding=result['encoding'])\n",
    "\n",
    "    # 处理 UniProt_name 列\n",
    "    df = df.assign(UniProt_name=df['UniProt_name'].str.split('|')).explode('UniProt_name')\n",
    "\n",
    "    # 仅保留 UniProt_name 列中包含 *_HUMAN 的行\n",
    "    df = df[df['UniProt_name'].str.contains('_HUMAN')]\n",
    "\n",
    "    # 保存处理后的 CSV 文件，并重命名\n",
    "    output_file_name = csv_file.replace('.csv', '_processed.csv')\n",
    "    output_file_path = os.path.join(output_folder, output_file_name)\n",
    "    \n",
    "    df.to_csv(output_file_path, index=False)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 去重保留uniprot name唯一值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '02_ingredients_targets/022_ingredients_targets_processed'\n",
    "output_folder = '02_ingredients_targets/023_ingredients_targets_uniquedata'\n",
    "\n",
    "# Create output folder if it doesn't exist\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# List all files in the input folder\n",
    "all_files = os.listdir(input_folder)\n",
    "\n",
    "# Filter only CSV files\n",
    "csv_files = [file for file in all_files if file.endswith('.csv')]\n",
    "\n",
    "for csv_file in csv_files:\n",
    "    input_file_path = os.path.join(input_folder, csv_file)\n",
    "    output_file_path = os.path.join(output_folder, csv_file)\n",
    "\n",
    "    # Detect file encoding\n",
    "    with open(input_file_path, 'rb') as f:\n",
    "        result = chardet.detect(f.read())\n",
    "\n",
    "    # Read file with the detected encoding\n",
    "    df = pd.read_csv(input_file_path, index_col=None, header=0, encoding=result['encoding'])\n",
    "\n",
    "    # Drop duplicates in 'UniProt_name' column and keep only unique values\n",
    "    df = df[['UniProt_name']].drop_duplicates()\n",
    "\n",
    "    # Save the processed dataframe to a new CSV file\n",
    "    output_file_path = output_file_path.replace('.csv', '_uniquedata.csv')\n",
    "    df.to_csv(output_file_path, index=False)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 核准Uniprot reviewed蛋白"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## unipor rest api 物种为人，状态为reviewed的蛋白质序列\n",
    "url = 'https://rest.uniprot.org/uniprotkb/stream?format=fasta&query=%28%2A%29%20AND%20%28reviewed%3Atrue%29%20AND%20%28model_organism%3A9606%29'\n",
    "all_fastas = requests.get(url).text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result exported to 04.Final/ingredient_output.csv\n"
     ]
    }
   ],
   "source": [
    "input_folder = '02_ingredients_targets/023_ingredients_targets_uniquedata'\n",
    "output_folder = '04.Final'\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 更改导出文件名\n",
    "base_name = os.path.basename(csv_file)\n",
    "output_csv = os.path.join(output_folder, base_name.replace('_uniquedata.csv', '_adjusted.csv'))\n",
    "\n",
    "# 通过gene name匹配fasta文件的entry name\n",
    "# 读取 csv 文件\n",
    "csv_files = glob.glob(os.path.join(input_folder, '*_uniquedata.csv'))\n",
    "df = pd.read_csv(csv_file)\n",
    "fasta_list = re.split(r'\\n(?=>)', all_fastas)\n",
    "# 获取 csv 表格文件中 UniProt_name 列的值列表\n",
    "symbol_values = df['UniProt_name'].tolist()\n",
    "# 构建正则表达式模式\n",
    "pattern = '|'.join([f'\\\\b{symbol}\\\\b' for symbol in symbol_values])\n",
    "# 使用正则表达式模式匹配 fasta 列表\n",
    "matched_fastas = [fasta for fasta in fasta_list if re.search(pattern, fasta)]\n",
    "# 创建包含 fasta 数据的 DataFrame\n",
    "fasta_df = pd.DataFrame({'fasta': matched_fastas})\n",
    "\n",
    "# 除标题行外，每行仅保留第二个“|”后和第一个空格前的内容\n",
    "# 处理每行数据，提取所需内容\n",
    "processed_data = []\n",
    "original_values = []\n",
    "for index, row in fasta_df.iterrows():\n",
    "    value = row['fasta']\n",
    "    value_parts = value.split('|')\n",
    "    if len(value_parts) > 2:\n",
    "        processed_value = value_parts[2].split(' ')[0]\n",
    "        original_value = [match for match in symbol_values if match in value]\n",
    "    else:\n",
    "        processed_value = ''\n",
    "        original_value = []\n",
    "    processed_data.append(processed_value)\n",
    "    original_values.append(original_value)\n",
    "\n",
    "# 创建新的 DataFrame，并将处理后的数据存入其中\n",
    "processed_df = pd.DataFrame({\n",
    "    'Original Value': original_values, \n",
    "    'Processed Value': processed_data\n",
    "})\n",
    "\n",
    "# 导出为 CSV 文件\n",
    "processed_df.to_csv(output_csv, index=False)\n",
    "print(f\"Result exported to {output_csv}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 DISEASE-TARGETS"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 获取原始信息"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用DisGeNET REST API 获取DisGeNET数据库疾病靶点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "结果已保存至文件：01.orig/disgenet_results.xml\n"
     ]
    }
   ],
   "source": [
    "# 设置导出文件夹路径和导出文件名\n",
    "output_folder = \"03_diseases_targets/031_orig\"  # 路径可根据需要更改\n",
    "output_filename = \"disgenet_results.xml\"  # 文件名可根据需要更改\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "output_file = os.path.join(output_folder, output_filename)\n",
    "\n",
    "# 输入疾病信息\n",
    "vocabulary = \"mesh\" # ICD9CM, ICD10, MeSH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO identifier\n",
    "disease_id = \"D001172\" # Disease id or list of disease ids separated by \",\" up to 100.\n",
    "\n",
    "# 设置API密钥\n",
    "token = \"1e0082cbe4be2f5cc81b1b9c8876d8a577cfd697\" # 请替换为你已获得授权的DisGeNET_REST_API密钥\n",
    "headers = {\"Authorization\": f\"Bearer {token}\"}\n",
    "\n",
    "# 发送请求，获取GDA数据\n",
    "url = f\"https://www.disgenet.org/api/gda/disease/{vocabulary}/{disease_id}?format=xml\"  # 共有三种格式：TSV, JSON, XML\n",
    "response = requests.get(url, headers=headers)\n",
    "\n",
    "# 如果文件夹不存在则创建\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 输出结果到文件\n",
    "with open(output_file, \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(response.text)\n",
    "    \n",
    "print(f\"结果已保存至文件：{output_file}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 数据初处理"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DisGeNET数据库xml转csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查目录是否存在，如果不存在，则创建它\n",
    "output_dir = '03_diseases_targets/032_trans'\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "# 解析XML文件\n",
    "tree = ET.parse('03_diseases_targets/031_orig/disgenet_results.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "# 打开CSV文件并写入标题行\n",
    "with open(os.path.join(output_dir, 'disgenet_results.csv'), 'w', newline='') as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerow(['geneid', 'gene_symbol', 'uniprotid', 'gene_dsi', 'gene_dpi', 'gene_pli', 'protein_class', 'protein_class_name', 'diseaseid', 'disease_name', 'disease_class', 'disease_class_name', 'disease_type', 'disease_semantic_type', 'score', 'ei', 'el', 'year_initial', 'year_final', 'source'])\n",
    "\n",
    "    # 遍历XML文件并将值写入CSV文件\n",
    "    for item in root.findall('.//list-item'):\n",
    "        geneid = item.find('geneid').text\n",
    "        gene_symbol = item.find('gene_symbol').text\n",
    "        uniprotid = item.find('uniprotid').text\n",
    "        gene_dsi = item.find('gene_dsi').text\n",
    "        gene_dpi = item.find('gene_dpi').text\n",
    "        gene_pli = item.find('gene_pli').text\n",
    "        protein_class = item.find('protein_class').text\n",
    "        protein_class_name = item.find('protein_class_name').text\n",
    "        diseaseid = item.find('diseaseid').text\n",
    "        disease_name = item.find('disease_name').text\n",
    "        disease_class = item.find('disease_class').text\n",
    "        disease_class_name = item.find('disease_class_name').text\n",
    "        disease_type = item.find('disease_type').text\n",
    "        disease_semantic_type = item.find('disease_semantic_type').text\n",
    "        score = item.find('score').text\n",
    "        ei = item.find('ei').text\n",
    "        el = item.find('el').text\n",
    "        year_initial = item.find('year_initial').text\n",
    "        year_final = item.find('year_final').text\n",
    "        source = item.find('source').text\n",
    "\n",
    "        # 将值写入CSV文件\n",
    "        writer.writerow([geneid, gene_symbol, uniprotid, gene_dsi, gene_dpi, gene_pli, protein_class, protein_class_name, diseaseid, disease_name, disease_class, disease_class_name, disease_type, disease_semantic_type, score, ei, el, year_initial, year_final, source])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 其他tsv, txt转csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输入文件夹路径\n",
    "input_folder = \"03_diseases_targets/031_orig\"\n",
    "output_folder = \"03_diseases_targets/032_trans\"\n",
    "\n",
    "# 创建输出文件夹\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 遍历所有文件\n",
    "for file in os.listdir(input_folder):\n",
    "    if file.endswith('.tsv') or file.endswith('.txt'):\n",
    "        # 读取tsv文件内容\n",
    "        with open(os.path.join(input_folder, file), 'r', encoding='utf-8') as f:\n",
    "            tsv_reader = csv.reader(f, delimiter='\\t')\n",
    "            rows = [row for row in tsv_reader]\n",
    "\n",
    "        # 将tsv文件内容写入csv文件\n",
    "        with open(os.path.join(output_folder, file.replace('.tsv', '.csv').replace('.txt', '.csv')), 'w', newline='', encoding='utf-8') as f:\n",
    "            csv_writer = csv.writer(f)\n",
    "            csv_writer.writerows(rows)\n",
    "    elif file.endswith('.csv'):\n",
    "        # 直接复制csv文件到输出文件夹\n",
    "        shutil.copyfile(os.path.join(input_folder, file), os.path.join(output_folder, file))\n",
    "    elif file.endswith('.xlsx'):\n",
    "        # 读取xlsx文件\n",
    "        df = pd.read_excel(os.path.join(input_folder, file))\n",
    "\n",
    "        # 将xlsx文件内容写入csv文件\n",
    "        csv_file = os.path.join(output_folder, file.replace('.xlsx', '.csv'))\n",
    "        df.to_csv(csv_file, index=False, encoding='utf-8')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 疾病靶点筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '03_diseases_targets/032_trans'\n",
    "output_folder = '03_diseases_targets/033_screened'\n",
    "\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "## 以下为筛选条件\n",
    "CTD_inferencescore_threshold = 100 # 文件名含有“CTD”的文件筛选条件\n",
    "disgenet_score_threshold = 0.1 # 文件名含有“disgenet”的文件筛选条件\n",
    "GeneCards_Relevancescore_threshold = 10 # 文件名含有“GeneCards”的文件筛选条件\n",
    "NCBI_org_name = \"Homo sapiens\" # 文件名含有“NCBI”的文件筛选条件\n",
    "Opentargets_overallAssociationScore_threshold = 0.1 # 文件名含有“Opentargets”的文件筛选条件\n",
    "\n",
    "for file in os.listdir(input_folder):\n",
    "    if file.endswith('.csv') and 'CTD' in file:\n",
    "        output_file = os.path.join(output_folder, file)\n",
    "        with open(os.path.join(input_folder, file), 'r', newline='', encoding='utf-8') as f_input:\n",
    "            with open(output_file, 'w', newline='', encoding='utf-8') as f_output:\n",
    "                reader = csv.reader(f_input)\n",
    "                writer = csv.writer(f_output)\n",
    "                header = next(reader)\n",
    "                writer.writerow(header)\n",
    "                \n",
    "                direct_evidence_index = header.index(\"Direct Evidence\")\n",
    "                inference_score_index = header.index(\"Inference Score\")\n",
    "                \n",
    "                for row in reader:\n",
    "                    if row[direct_evidence_index] != \"\": \n",
    "                        writer.writerow(row)\n",
    "                    elif float(row[inference_score_index]) >= CTD_inferencescore_threshold: \n",
    "                        writer.writerow(row)\n",
    "                        \n",
    "    elif file.endswith('.csv') and 'disgenet' in file:\n",
    "        output_file = os.path.join(output_folder, file)\n",
    "        with open(os.path.join(input_folder, file), 'r', newline='', encoding='utf-8') as f_input:\n",
    "            with open(output_file, 'w', newline='', encoding='utf-8') as f_output:\n",
    "                reader = csv.reader(f_input)\n",
    "                writer = csv.writer(f_output)\n",
    "                header = next(reader)\n",
    "                writer.writerow(header)\n",
    "                \n",
    "                score_index = header.index(\"score\")\n",
    "                \n",
    "                for row in reader:\n",
    "                    if float(row[score_index]) >= disgenet_score_threshold: \n",
    "                        writer.writerow(row)\n",
    "                        \n",
    "    elif file.endswith('.csv') and 'GeneCards' in file:\n",
    "        output_file = os.path.join(output_folder, file)\n",
    "        with open(os.path.join(input_folder, file), 'r', newline='', encoding='utf-8') as f_input:\n",
    "            with open(output_file, 'w', newline='', encoding='utf-8') as f_output:\n",
    "                reader = csv.reader(f_input)\n",
    "                writer = csv.writer(f_output)\n",
    "                header = next(reader)\n",
    "                writer.writerow(header)\n",
    "                \n",
    "                relevance_score_index = header.index(\"Relevance score\")\n",
    "                \n",
    "                for row in reader:\n",
    "                    if float(row[relevance_score_index]) >= GeneCards_Relevancescore_threshold: \n",
    "                        writer.writerow(row)\n",
    "                        \n",
    "    elif file.endswith('.csv') and 'NCBI' in file:\n",
    "        output_file = os.path.join(output_folder, file)\n",
    "        with open(os.path.join(input_folder, file), 'r', newline='', encoding='utf-8') as f_input:\n",
    "            with open(output_file, 'w', newline='', encoding='utf-8') as f_output:\n",
    "                reader = csv.reader(f_input)\n",
    "                writer = csv.writer(f_output)\n",
    "                header = next(reader)\n",
    "                writer.writerow(header)\n",
    "                \n",
    "                org_name_index = header.index(\"Org_name\")\n",
    "                \n",
    "                for row in reader:\n",
    "                    if row[org_name_index] == NCBI_org_name: \n",
    "                        writer.writerow(row)\n",
    "\n",
    "for file in os.listdir(input_folder):\n",
    "    if file.endswith('.csv') and 'Opentargets' in file:\n",
    "        output_file = os.path.join(output_folder, file)\n",
    "        with open(os.path.join(input_folder, file), 'r', newline='', encoding='utf-8') as f_input:\n",
    "            with open(output_file, 'w', newline='', encoding='utf-8') as f_output:\n",
    "                reader = csv.reader(f_input)\n",
    "                writer = csv.writer(f_output)\n",
    "                header = next(reader)\n",
    "                writer.writerow(header)\n",
    "                \n",
    "                overall_score_index = header.index(\"overallAssociationScore\")\n",
    "                \n",
    "                for row in reader:\n",
    "                    if float(row[overall_score_index]) >= Opentargets_overallAssociationScore_threshold:\n",
    "                        writer.writerow(row)\n",
    "    elif file.endswith('.csv') and 'OMIM' in file:\n",
    "        output_file = os.path.join(output_folder, file)\n",
    "        shutil.copyfile(os.path.join(input_folder, file), output_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 疾病靶点合并并去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义输入输出文件夹路径\n",
    "input_folder = '03_diseases_targets/033_screened'\n",
    "output_folder = '03_diseases_targets/033_uniquedata'\n",
    "\n",
    "# 创建输出文件夹\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 创建一个空列表，用于存储所有含有“symbol”列的值\n",
    "all_symbol_values = []\n",
    "\n",
    "# 遍历所有CSV文件\n",
    "for file in os.listdir(input_folder):\n",
    "    if file.endswith('.csv'):\n",
    "        # 读取CSV文件内容\n",
    "        with open(os.path.join(input_folder, file), 'r', encoding='utf-8') as f:\n",
    "            csv_reader = csv.reader(f)\n",
    "            header = next(csv_reader)\n",
    "            symbol_columns = [i for i, col in enumerate(header) if 'symbol' in col.lower()]\n",
    "            # 如果CSV文件中存在含有“symbol”列\n",
    "            if symbol_columns:\n",
    "                # 遍历所有行\n",
    "                for row in csv_reader:\n",
    "                    # 将含有“symbol”列的值添加到all_symbol_values列表中\n",
    "                    for col_index in symbol_columns:\n",
    "                        all_symbol_values.append(row[col_index])\n",
    "\n",
    "# 将all_symbol_values列表去重，仅保留唯一值\n",
    "unique_symbol_values = list(set(all_symbol_values))\n",
    "\n",
    "# 将所有唯一值合并为一个长字符串\n",
    "merged_values = ', '.join(unique_symbol_values)\n",
    "\n",
    "# 将合并后的值写入到输出文件中\n",
    "output_file = os.path.join(output_folder, 'disease_targets_uniquedata.csv')\n",
    "with open(output_file, 'w', newline='', encoding='utf-8') as f:\n",
    "    csv_writer = csv.writer(f)\n",
    "    csv_writer.writerow(['Symbol'])\n",
    "    for symbol in merged_values.split(','):\n",
    "        csv_writer.writerow([symbol.strip()])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Uniprot 基因entry name 核对"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### uniprot api url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "## unipor rest api 物种为人，状态为reviewed的蛋白质序列\n",
    "url = 'https://rest.uniprot.org/uniprotkb/stream?format=fasta&query=%28%2A%29%20AND%20%28reviewed%3Atrue%29%20AND%20%28model_organism%3A9606%29'\n",
    "all_fastas = requests.get(url).text\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "fasta_list = re.split(r'\\n(?=>)', all_fastas)\n",
    "[fasta for fasta in fasta_list if 'DUS3_HUMAN' in fasta]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 成分靶点校正"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '02_ingredients_targets/023_ingredients_targets_uniquedata'\n",
    "output_folder = '04_intersection_targets'\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 更改导出文件名\n",
    "base_name = os.path.basename(csv_file)\n",
    "output_csv = os.path.join(output_folder, base_name.replace('_uniquedata.csv', '_adjusted.csv'))\n",
    "\n",
    "# 通过gene name匹配fasta文件的entry name\n",
    "# 读取 csv 文件\n",
    "csv_files = glob.glob(os.path.join(input_folder, '*_uniquedata.csv'))\n",
    "df = pd.read_csv(csv_file)\n",
    "fasta_list = re.split(r'\\n(?=>)', all_fastas)\n",
    "# 获取 csv 表格文件中 UniProt_name 列的值列表\n",
    "symbol_values = df['UniProt_name'].tolist()\n",
    "# 构建正则表达式模式\n",
    "pattern = '|'.join([f'\\\\b{symbol}\\\\b' for symbol in symbol_values])\n",
    "# 使用正则表达式模式匹配 fasta 列表\n",
    "matched_fastas = [fasta for fasta in fasta_list if re.search(pattern, fasta)]\n",
    "# 创建包含 fasta 数据的 DataFrame\n",
    "fasta_df = pd.DataFrame({'fasta': matched_fastas})\n",
    "\n",
    "# 除标题行外，每行仅保留第二个“|”后和第一个空格前的内容\n",
    "# 处理每行数据，提取所需内容\n",
    "processed_data = []\n",
    "original_values = []\n",
    "for index, row in fasta_df.iterrows():\n",
    "    value = row['fasta']\n",
    "    value_parts = value.split('|')\n",
    "    if len(value_parts) > 2:\n",
    "        processed_value = value_parts[2].split(' ')[0]\n",
    "        original_value = [match for match in symbol_values if match in value]\n",
    "    else:\n",
    "        processed_value = ''\n",
    "        original_value = []\n",
    "    processed_data.append(processed_value)\n",
    "    original_values.append(original_value)\n",
    "\n",
    "# 创建新的 DataFrame，并将处理后的数据存入其中\n",
    "processed_df = pd.DataFrame({\n",
    "    'Original Value': original_values, \n",
    "    'Processed Value': processed_data\n",
    "})\n",
    "\n",
    "# 导出为 CSV 文件\n",
    "processed_df.to_csv(output_csv, index=False)\n",
    "print(f\"Result exported to {output_csv}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 疾病靶点校正"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result exported to 04.Final/output.csv\n"
     ]
    }
   ],
   "source": [
    "csv_file = '03_diseases_targets/033_uniquedata/disease_targets_uniquedata.csv'\n",
    "output_csv = '04_intersection_targets/disease_targets_adjusted.csv'\n",
    "\n",
    "# 通过gene name匹配fasta文件的entry name\n",
    "# 读取 csv 文件\n",
    "df = pd.read_csv(csv_file)\n",
    "fasta_list = re.split(r'\\n(?=>)', all_fastas)\n",
    "# 获取 csv 表格文件中 symbol 列的值列表\n",
    "symbol_values = df['Symbol'].tolist()\n",
    "# 构建正则表达式模式\n",
    "pattern = '|'.join([f'GN={symbol} ' for symbol in symbol_values])\n",
    "\n",
    "# 使用正则表达式模式匹配 fasta 列表\n",
    "matched_fastas = []\n",
    "matched_symbols = []\n",
    "for fasta in fasta_list:\n",
    "    match = re.search(pattern, fasta)\n",
    "    if match:\n",
    "        symbol = match.group().split('=')[1]  # 获取匹配到的gene symbol\n",
    "        matched_fastas.append(fasta)\n",
    "        matched_symbols.append(symbol)\n",
    "\n",
    "# 创建包含 fasta 数据的 DataFrame\n",
    "fasta_df = pd.DataFrame({'fasta': matched_fastas, 'Symbol': matched_symbols})\n",
    "\n",
    "# 除标题行外，每行仅保留第二个“|”后和第一个空格前的内容\n",
    "# 处理每行数据，提取所需内容\n",
    "processed_data = []\n",
    "for index, row in fasta_df.iterrows():\n",
    "    value = row['fasta']\n",
    "    value_parts = value.split('|')\n",
    "    if len(value_parts) > 2:\n",
    "        processed_value = value_parts[2].split(' ')[0]\n",
    "    else:\n",
    "        processed_value = ''\n",
    "    processed_data.append(processed_value)\n",
    "\n",
    "# 创建新的 DataFrame，并将处理后的数据和匹配到的gene symbol存入其中\n",
    "processed_df = pd.DataFrame({\n",
    "    'Original Gene Symbol': fasta_df['Symbol'],\n",
    "    'Processed Value': processed_data\n",
    "})\n",
    "\n",
    "# 导出为 CSV 文件\n",
    "processed_df.to_csv(output_csv, index=False)\n",
    "print(f\"Result exported to {output_csv}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 成分疾病靶点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def detect_encoding(file_path):\n",
    "    with open(file_path, 'rb') as f:\n",
    "        result = chardet.detect(f.read())\n",
    "    return result['encoding']\n",
    "\n",
    "path = '04_intersection_targets'\n",
    "output_path = '04_intersection_targets'\n",
    "csv_files = glob.glob(os.path.join(path, '*.csv'))\n",
    "\n",
    "uni_sets = []\n",
    "\n",
    "for csv_file in csv_files:\n",
    "    encoding = detect_encoding(csv_file)\n",
    "    df = pd.read_csv(csv_file, encoding=encoding)\n",
    "    uni_set = set(df['Processed Value'])\n",
    "    uni_sets.append(uni_set)\n",
    "\n",
    "intersection = set.intersection(*uni_sets)\n",
    "result_df = pd.DataFrame(list(intersection), columns=['Processed Value'])\n",
    "result_df.to_csv(os.path.join(output_path, 'intersection_targets.csv'), index=False)\n",
    "\n",
    "# 绘制 Venn 图（仅适用于 2 个 CSV 文件的情况）\n",
    "if len(uni_sets) == 2:\n",
    "    venn = venn2([uni_sets[0], uni_sets[1]], set_labels=['File 1', 'File 2'])\n",
    "    plt.savefig(os.path.join(output_path, 'venn_diagram.svg'), format='svg')\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"Venn diagram can only be plotted for 2 CSV files.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 BIOINFORMATICS ANALYSIS"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 STRING PPI"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STRING PPI 图SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving interaction network to output\\all_genes_network.svg\n"
     ]
    }
   ],
   "source": [
    "output_folder = \"05_ppi\" # 定义输出文件夹路径\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "string_api_url = \"https://version-11-5.string-db.org/api\"\n",
    "output_format = \"svg\"\n",
    "method = \"network\"\n",
    "\n",
    "df = pd.read_csv(\"04_intersection_targets/intersection_targets.csv\") # 读取 CSV 文件\n",
    "my_genes = df[\"Processed Value\"].tolist()\n",
    "\n",
    "identifiers = \"%0d\".join(my_genes)\n",
    "\n",
    "request_url = \"/\".join([string_api_url, output_format, method])\n",
    "\n",
    "params = {\n",
    "    \"identifiers\": identifiers,\n",
    "    \"species\": 9606,\n",
    "    \"required_score\": 900,  #对应STRING网页中的\"Minimum required interaction score\"，可选400，700，900\n",
    "    \"hide_disconnected_nodes\": 1,\n",
    "    \"caller_identity\": \"www.my_app.org\"\n",
    "}\n",
    "\n",
    "response = requests.post(request_url, data=params)\n",
    "\n",
    "file_name = \"all_genes_network.svg\"\n",
    "output_path = os.path.join(output_folder, file_name)\n",
    "print(f\"Saving interaction network to {output_path}\")\n",
    "\n",
    "with open(output_path, 'wb') as fh:\n",
    "    fh.write(response.content)\n",
    "\n",
    "sleep(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STRING TSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein interactions saved to output\\protein_interactions.tsv\n"
     ]
    }
   ],
   "source": [
    "output_folder = \"05_ppi\" # 请根据实际情况修改输出文件夹路径\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "string_api_url = \"https://string-db.org/api\"\n",
    "output_format = \"tsv\"\n",
    "method = \"network\"\n",
    "\n",
    "csv_file_path = \"04_intersection_targets/intersection_targets.csv\"  # 请根据实际情况修改 CSV 文件路径\n",
    "df = pd.read_csv(csv_file_path)\n",
    "my_genes = df[\"Processed Value\"].tolist()\n",
    "\n",
    "request_url = \"/\".join([string_api_url, output_format, method])\n",
    "\n",
    "params = {\n",
    "    \"identifiers\": \"%0d\".join(my_genes),\n",
    "    \"species\": 9606,\n",
    "    \"required_score\": 900, #对应STRING网页中的\"Minimum required interaction score\"，可选400，700，900\n",
    "    \"caller_identity\": \"www.my_example_app.org\",\n",
    "}\n",
    "\n",
    "response = requests.post(request_url, data=params)\n",
    "\n",
    "\n",
    "# 保存为 TSV 文件\n",
    "output_file_path = os.path.join(output_folder, \"protein_interactions.tsv\")\n",
    "with open(output_file_path, \"w\") as f:\n",
    "    f.write(response.text)\n",
    "\n",
    "print(f\"Protein interactions saved to {output_file_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 python的networkx包_网络拓扑分析 the network topology properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppi_data = pd.read_csv(\"05_ppi/protein_interactions.tsv\", sep=\"\\t\")\n",
    "output_folder = \"05_ppi\"\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "# 创建一个空的无向加权网络\n",
    "G = nx.Graph()\n",
    "\n",
    "# 添加节点和边\n",
    "for index, row in ppi_data.iterrows():\n",
    "    G.add_edge(row[\"preferredName_A\"], row[\"preferredName_B\"], weight=row[\"score\"])\n",
    "\n",
    "# 计算网络指标\n",
    "degree_centrality = nx.degree_centrality(G)\n",
    "closeness_centrality = nx.closeness_centrality(G)\n",
    "betweenness_centrality = nx.betweenness_centrality(G)\n",
    "eigenvector_centrality = nx.eigenvector_centrality(G)\n",
    "\n",
    "# 将所有中心性指标组合成一个DataFrame\n",
    "centrality_measures = {\n",
    "    'degree': degree_centrality,\n",
    "    'closeness': closeness_centrality,\n",
    "    'betweenness': betweenness_centrality,\n",
    "    'eigenvector': eigenvector_centrality\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(centrality_measures)\n",
    "\n",
    "# 计算总和并按总和排序\n",
    "df['sum'] = df.sum(axis=1)\n",
    "df = df.sort_values('sum', ascending=False)\n",
    "\n",
    "# 将结果保存为 CSV 文件\n",
    "df.to_csv(f\"{output_folder}/centrality_measures.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cytoscape_图"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 富集分析 Enrichment Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取CSV文件\n",
    "output_folder = \"05_ppi\"\n",
    "csv_file = \"05_ppi/centrality_measures.csv\"\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "# 只保留Protein列\n",
    "# df = df[[\"Protein\"]]\n",
    "df = df.iloc[:, 0:1]\n",
    "\n",
    "# 只保留前20个值\n",
    "df = df.head(20)\n",
    "\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# 导出为XLSX文件\n",
    "output_file = output_folder + \"selected_proteins.xlsx\"\n",
    "df.to_excel(output_file, index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 R语言处理准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'openxlsx' was built under R version 4.2.3\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 4.2.3\"\n",
      "Warning message:\n",
      "\"package 'stringr' was built under R version 4.2.3\"\n",
      "Warning message:\n",
      "\"package 'enrichplot' was built under R version 4.2.3\"\n",
      "\n",
      "\n",
      "clusterProfiler v4.6.2  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/\n",
      "\n",
      "If you use clusterProfiler in published research, please cite:\n",
      "T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141\n",
      "\n",
      "\n",
      "Attaching package: 'clusterProfiler'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:stats':\n",
      "\n",
      "    filter\n",
      "\n",
      "\n",
      "Warning message:\n",
      "\"package 'GOplot' was built under R version 4.2.3\"\n",
      "Loading required package: ggdendro\n",
      "\n",
      "Warning message:\n",
      "\"package 'ggdendro' was built under R version 4.2.3\"\n",
      "Loading required package: gridExtra\n",
      "\n",
      "Warning message:\n",
      "\"package 'gridExtra' was built under R version 4.2.3\"\n",
      "Loading required package: RColorBrewer\n",
      "\n",
      "DOSE v3.24.2  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/\n",
      "\n",
      "If you use DOSE in published research, please cite:\n",
      "Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609\n",
      "\n",
      "\n",
      "Warning message:\n",
      "\"package 'ggnewscale' was built under R version 4.2.3\"\n",
      "Loading required package: BiocGenerics\n",
      "\n",
      "\n",
      "Attaching package: 'BiocGenerics'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:gridExtra':\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n",
      "    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n",
      "    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n",
      "    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n",
      "    Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n",
      "    table, tapply, union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "Loading required package: graph\n",
      "\n",
      "\n",
      "Attaching package: 'graph'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:stringr':\n",
      "\n",
      "    boundary\n",
      "\n",
      "\n",
      "Loading required package: Biobase\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "Loading required package: GO.db\n",
      "\n",
      "Loading required package: AnnotationDbi\n",
      "\n",
      "Loading required package: stats4\n",
      "\n",
      "Loading required package: IRanges\n",
      "\n",
      "Loading required package: S4Vectors\n",
      "\n",
      "\n",
      "Attaching package: 'S4Vectors'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:clusterProfiler':\n",
      "\n",
      "    rename\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    expand.grid, I, unname\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'IRanges'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:clusterProfiler':\n",
      "\n",
      "    slice\n",
      "\n",
      "\n",
      "The following object is masked from 'package:grDevices':\n",
      "\n",
      "    windows\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'AnnotationDbi'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:clusterProfiler':\n",
      "\n",
      "    select\n",
      "\n",
      "\n",
      "Loading required package: SparseM\n",
      "\n",
      "\n",
      "Attaching package: 'SparseM'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:base':\n",
      "\n",
      "    backsolve\n",
      "\n",
      "\n",
      "\n",
      "groupGOTerms: \tGOBPTerm, GOMFTerm, GOCCTerm environments built.\n",
      "\n",
      "\n",
      "Attaching package: 'topGO'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:IRanges':\n",
      "\n",
      "    members\n",
      "\n",
      "\n",
      "Warning message:\n",
      "\"package 'circlize' was built under R version 4.2.3\"\n",
      "========================================\n",
      "circlize version 0.4.15\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: https://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization\n",
      "  in R. Bioinformatics 2014.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(circlize))\n",
      "========================================\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'circlize'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:graph':\n",
      "\n",
      "    degree\n",
      "\n",
      "\n",
      "Loading required package: grid\n",
      "\n",
      "\n",
      "Attaching package: 'grid'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:topGO':\n",
      "\n",
      "    depth\n",
      "\n",
      "\n",
      "========================================\n",
      "ComplexHeatmap version 2.14.0\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite either one:\n",
      "- Gu, Z. Complex Heatmap Visualization. iMeta 2022.\n",
      "- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "    genomic data. Bioinformatics 2016.\n",
      "\n",
      "\n",
      "The new InteractiveComplexHeatmap package can directly export static \n",
      "complex heatmaps into an interactive Shiny app with zero effort. Have a try!\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
      "\n",
      "\n",
      "Warning message:\n",
      "\"package 'repr' was built under R version 4.2.3\"\n"
     ]
    }
   ],
   "source": [
    "library(openxlsx)#读取.xlsx文件\n",
    "library(ggplot2)#柱状图和点状图\n",
    "library(stringr)#基因ID转换\n",
    "library(enrichplot)#GO,KEGG,GSEA\n",
    "library(clusterProfiler)#GO,KEGG,GSEA\n",
    "library(GOplot)#弦图，弦表图，系统聚类图\n",
    "library(DOSE)\n",
    "library(ggnewscale)\n",
    "library(topGO)#绘制通路网络图\n",
    "library(circlize)#绘制富集分析圈图\n",
    "library(ComplexHeatmap)#绘制图例\n",
    "library(repr)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取gene name数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed:.0\n",
      "1       STAT3\n",
      "2        RELA\n",
      "3        ESR1\n",
      "4       MAPK1\n",
      "5      PTPN11\n",
      "6         JUN\n",
      "7       HIF1A\n",
      "8       NFKB1\n",
      "9       HDAC1\n",
      "10        FOS\n",
      "11       TLR4\n",
      "12     CXCL12\n",
      "13       JAK1\n",
      "14       JAK3\n",
      "15      CXCR4\n",
      "16      PRKCD\n",
      "17       MTOR\n",
      "18      PTPN2\n",
      "19       CHUK\n",
      "20       NOS3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'select()' returned 1:1 mapping between keys and columns\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   SYMBOL ENTREZID\n",
      "1   STAT3     6774\n",
      "2    RELA     5970\n",
      "3    ESR1     2099\n",
      "4   MAPK1     5594\n",
      "5  PTPN11     5781\n",
      "6     JUN     3725\n",
      "7   HIF1A     3091\n",
      "8   NFKB1     4790\n",
      "9   HDAC1     3065\n",
      "10    FOS     2353\n",
      "11   TLR4     7099\n",
      "12 CXCL12     6387\n",
      "13   JAK1     3716\n",
      "14   JAK3     3718\n",
      "15  CXCR4     7852\n",
      "16  PRKCD     5580\n",
      "17   MTOR     2475\n",
      "18  PTPN2     5771\n",
      "19   CHUK     1147\n",
      "20   NOS3     4846\n"
     ]
    }
   ],
   "source": [
    "#载入差异表达数据，只需基因ID(GO,KEGG,GSEA需要)和Log2FoldChange(GSEA需要)即可\n",
    "info <- read.xlsx( \"05.ppiselected_proteins.xlsx\", rowNames = F,colNames = T)\n",
    "print(info)\n",
    "#指定富集分析的物种库\n",
    "GO_database <- 'org.Hs.eg.db' #GO分析指定物种，物种缩写索引表详见http://bioconductor.org/packages/release/BiocViews.html#___OrgDb\n",
    "KEGG_database <- 'hsa' #KEGG分析指定物种，物种缩写索引表详见http://www.genome.jp/kegg/catalog/org_list.html\n",
    "\n",
    "#gene ID转换\n",
    "gene <- bitr(info$'Unnamed:.0',fromType = 'SYMBOL',toType = 'ENTREZID',OrgDb = GO_database)\n",
    "print(gene) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 GO分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#\n",
      "# over-representation test\n",
      "#\n",
      "#...@organism \t Homo sapiens \n",
      "#...@ontology \t GOALL \n",
      "#...@keytype \t ENTREZID \n",
      "#...@gene \t chr [1:20] \"6774\" \"5970\" \"2099\" \"5594\" \"5781\" \"3725\" \"3091\" \"4790\" \"3065\" ...\n",
      "#...pvalues adjusted by 'BH' with cutoff <0.05 \n",
      "#...1060 enriched terms found\n",
      "'data.frame':\t1060 obs. of  10 variables:\n",
      " $ ONTOLOGY   : chr  \"BP\" \"BP\" \"BP\" \"BP\" ...\n",
      " $ ID         : chr  \"GO:0043434\" \"GO:0062197\" \"GO:1901653\" \"GO:0034599\" ...\n",
      " $ Description: chr  \"response to peptide hormone\" \"cellular response to chemical stress\" \"cellular response to peptide\" \"cellular response to oxidative stress\" ...\n",
      " $ GeneRatio  : chr  \"11/20\" \"10/20\" \"10/20\" \"9/20\" ...\n",
      " $ BgRatio    : chr  \"421/18903\" \"345/18903\" \"373/18903\" \"288/18903\" ...\n",
      " $ pvalue     : num  8.24e-14 5.66e-13 1.23e-12 5.66e-12 9.75e-12 ...\n",
      " $ p.adjust   : num  1.63e-10 5.61e-10 8.13e-10 2.81e-09 3.87e-09 ...\n",
      " $ qvalue     : num  5.93e-11 2.04e-10 2.95e-10 1.02e-09 1.40e-09 ...\n",
      " $ geneID     : chr  \"STAT3/RELA/PTPN11/NFKB1/FOS/CXCL12/JAK1/JAK3/PRKCD/MTOR/PTPN2\" \"RELA/MAPK1/JUN/HIF1A/FOS/TLR4/PRKCD/MTOR/CHUK/NOS3\" \"STAT3/RELA/PTPN11/NFKB1/FOS/TLR4/JAK1/JAK3/PRKCD/PTPN2\" \"RELA/MAPK1/JUN/HIF1A/FOS/TLR4/PRKCD/CHUK/NOS3\" ...\n",
      " $ Count      : int  11 10 10 9 9 8 6 10 6 7 ...\n",
      "#...Citation\n",
      " T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.\n",
      " clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.\n",
      " The Innovation. 2021, 2(3):100141 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#设置导出文件夹路径\n",
    "output_dir <- \"06_enrichment\"\n",
    "#如果文件夹不存在，则创建\n",
    "if (!dir.exists(output_dir)) {\n",
    "  dir.create(output_dir)\n",
    "}\n",
    "#进行GO富集分析\n",
    "GO <- enrichGO(gene$ENTREZID,\n",
    "               OrgDb = GO_database,\n",
    "               keyType = \"ENTREZID\",\n",
    "               ont = \"ALL\",\n",
    "               pvalueCutoff = 0.05,\n",
    "               qvalueCutoff = 0.05,\n",
    "               readable = TRUE)\n",
    "print(GO)\n",
    "#将结果保存为CSV文件\n",
    "output_file <- file.path(output_dir, \"GO_enrichment_results.csv\")\n",
    "write.csv(GO, file = output_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 KEGG分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reading KEGG annotation online: \"https://rest.kegg.jp/link/hsa/pathway\"...\n",
      "\n",
      "Warning message in utils::download.file(url, quiet = TRUE, method = method, ...):\n",
      "\"the 'wininet' method is deprecated for http:// and https:// URLs\"\n",
      "Reading KEGG annotation online: \"https://rest.kegg.jp/list/pathway/hsa\"...\n",
      "\n",
      "Warning message in utils::download.file(url, quiet = TRUE, method = method, ...):\n",
      "\"the 'wininet' method is deprecated for http:// and https:// URLs\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#\n",
      "# over-representation test\n",
      "#\n",
      "#...@organism \t hsa \n",
      "#...@ontology \t KEGG \n",
      "#...@keytype \t kegg \n",
      "#...@gene \t chr [1:20] \"6774\" \"5970\" \"2099\" \"5594\" \"5781\" \"3725\" \"3091\" \"4790\" \"3065\" ...\n",
      "#...pvalues adjusted by 'BH' with cutoff <0.05 \n",
      "#...115 enriched terms found\n",
      "'data.frame':\t115 obs. of  9 variables:\n",
      " $ ID         : chr  \"hsa05235\" \"hsa04659\" \"hsa05161\" \"hsa05167\" ...\n",
      " $ Description: chr  \"PD-L1 expression and PD-1 checkpoint pathway in cancer\" \"Th17 cell differentiation\" \"Hepatitis B\" \"Kaposi sarcoma-associated herpesvirus infection\" ...\n",
      " $ GeneRatio  : chr  \"12/20\" \"11/20\" \"10/20\" \"10/20\" ...\n",
      " $ BgRatio    : chr  \"89/8521\" \"108/8521\" \"162/8521\" \"194/8521\" ...\n",
      " $ pvalue     : num  9.21e-20 1.23e-16 7.33e-13 4.50e-12 8.52e-12 ...\n",
      " $ p.adjust   : num  1.55e-17 1.03e-14 4.10e-11 1.89e-10 2.86e-10 ...\n",
      " $ qvalue     : num  4.75e-18 3.18e-15 1.26e-11 5.81e-11 8.79e-11 ...\n",
      " $ geneID     : chr  \"6774/5970/5594/5781/3725/3091/4790/2353/7099/3716/2475/1147\" \"6774/5970/5594/3725/3091/4790/2353/3716/3718/2475/1147\" \"6774/5970/5594/3725/4790/2353/7099/3716/3718/1147\" \"6774/5970/5594/3725/3091/4790/2353/3716/2475/1147\" ...\n",
      " $ Count      : int  12 11 10 10 9 8 8 9 7 7 ...\n",
      "#...Citation\n",
      " T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu.\n",
      " clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.\n",
      " The Innovation. 2021, 2(3):100141 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 设置导出文件夹路径\n",
    "output_dir <- \"06_enrichment\"\n",
    "\n",
    "\n",
    "KEGG<-enrichKEGG(gene$ENTREZID,#KEGG富集分析\n",
    "                 organism = KEGG_database,\n",
    "                 pvalueCutoff = 0.05,\n",
    "                 qvalueCutoff = 0.05)\n",
    "print(KEGG)\n",
    "# 将结果保存为CSV文件\n",
    "output_file <- file.path(output_dir, \"KEGG_enrichment_results.csv\")\n",
    "write.csv(KEGG, file = output_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 富集分析可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用options()函数设置图像宽度和高度（以像素为单位），设置会影响Jupyter Notebook中所有后续的R图形\n",
    "options(repr.plot.width = 10, repr.plot.height = 10)  # 设置宽度为10英寸，高度为6英寸"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化_柱状图、点状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Om9L1hSMIlmGDYBUE624mgrVzmczDOVldX0UsWIPVq6d0feEIgmXXuNwrXbEIFhS9YG1cEl61uU0vDyt3FuM3icuqnaVd6pJdswoplEM2zcLey86ec07dqr/ktAoTqMeG8Xn5UjLZFP2F3kn7TgPBsmsQLA/BupMuWP4wk/MdOPb6c+wdLM/CIS1fsDDcZcUoWGHIajJY0lmFo/BySeLCgS7Rpm0lbvrBysZOA8GyaxAsD8G6ky5Y2bl8MZj2lxaDHay9H7X2Bcn9pXPm8mGw9i451kfBwkLxkrDnZOeJJWECneHc3q+wv5pmA/v+pL8NfA9WiQXr1XMhWIulC9ahd625/ejWYvTKjzr7dw2rjp39y7p+sMKQskQTweo5h2JqSbeubo2D1ax83nzqhpMu2MOybLCH5SFYd9JP1DhYm5CH4ejeKVr9YLVnRIyDNXCmlgyDdcq32WA1ww0QrGUYBMtDsO7kWrASNzX6VcHK2sP1BOt1G+G0hudBsO7kSrCObjU1uleJGcEarmi0pJ+htUt3+YlgvXgjBOt5EKw7uRKsXXjfsCUTx7ByuQp5DMvfcJg+htVzppZM7GsNg9Ucw1oRrGUZBKsgWHdzJVgrd5Sjd/5tuY1/lzC8V1deHx10zy+/SzhwppZU2qm9dCiOzTGsZmHvXcLeZAmWbeOuXhEs8FwJVto7a1Seh1UdXkpOw2BVp1atp4I1cKaW+I9p+OFFf2lTH+E6yIW987B6kyVYxo17ekWwwHMlWG74AJcNWbVnurt1ty/UDdmKM90HK+45U0v8x0PatqnsXnYIr/7EwmKXtGe6D+ZIsEwbBItggYRg2Tbu6BXBgveDYBk3bu8VwYL3g2CZN27MFcGCN4Rg2Tdu7BXBgveDYC3CmF+r+duIGgQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQrLuNnwaeu40vNwgWmIBg3Wf8VPCsbVgwCBaYgGDdZfz0pzcWy+aDRbDAOATrDuOnIyzM6ikGwQITEKzbjXGv9MWy+WARLDAOwbrdIFivgGCBgGDdbEz1Sl0smw8WwYKbcW54IUreDu0ujVfpyQ5iEcG61ZjulbZYNh8sggU3My9YqWuGdpcmVllx7BYRrFsNgvUaCNZCmBesblBkeHXTxmXdIoJ1q0GwXgPBWghPC1ZvBMG60bjUK2WxbD5YBAtuZhCsXeqSXX1145JNuClfuepieKUXbusulTdnzmX5aJUu6RYRrBsNgvUiCNZC6AdrVR0vD1e3zcVtdUhqcyFYu+rm3WCVG7GEYN1qEKwXQbAWguso95Vcdi7Omcv98uRYHBO39xfLD/uQIdceau8uJf7o+t6lw1VWe2fFt4Hv4SYuB+urZ/YUCBZcoReslTuXi85u5Zf7F3m5v9gMLKaDVY0crzLjXcL7DfawXgTBWgi9l4SiXb0j56d8m10M1sa51fE4WmWeuO5MLIJ1o0GwXgTBWgiaYGXNwslgFdukvDE5DVd5FOc1EKxbjbt6ZfTBIlhwM4NgDZf7T2uX7vLT5WCVe1ObtHcMa7BqgnW7QbBeA8FaCL1grbrDUS68oMvduh4SDVYx1bqzOK+BYN1qEKzXQLAWQi9Ye//WYLGrDrpX7xLmVbuOzTGsU9EEq7mUVm8iDvewzlnzPmFBsO4w7umV0QeLYMHN9M/Dqo5W+eNRzoXL/k3CTX1g6+Dj5Hebqkw1l/btre2aKpJzu4hg3W7c0SujDxbBgpsZn+nu1vW+08ql1bmfa/+7F8IZDoe0yVR3qTrT/SBXGXK16XpFsO4wCNYrIFgLR/fbZrQQrDuM23tl9MEiWPBwCJahjdyYK6sPFsGCh0OwLG3kxl4ZfbAIFjwcgmVsI/NrNX8bX24QLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2NcCdYucy7bX9/ALpk5I4IFPQgWhsaIBuuUuEB2dQNufn4IFggIFobGiAYrcetTUeSJ213bAMGC+yBYGBojFqy9W4XPufMv+E5rF/pVx8l/dO60csnGX5hfLIIFAoKFoTFiwVq5Q3XhWP5/Di8Pk3M/WGHhhmDBvRAsDI0RC1YvQht/JCtzm36wsnOx8/tfvCSE+yBYGBpDHazUlS8HTy4dvCRsL82eE8ECAcHC0BjqYMlMXbo0C4IFAoKFoTFUx7CKA8GC50KwMDSG5l3CQ7IevSQ8ESx4JAQLQ2PozsM6yoPuidsX54xgwSMhWBgaI36me1qd6V5WSpzWsPEXtsNgzf7ZHIIFAoKFoTGu/Cxhvk6anyVsTxwtNonbDo5h7QgW3AXBwtAY/LYGMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDTGnGDd8gfpI2t73Kpg+RAsDI2hD5armb3xS+t71IrgHSBYGBpDHSznrhVrZs8IFggIFobGeGCwqmHqOREsEBCsu42fBJ67jS83tMFyTlWsGRUiWCAgWPcZPxE8axsWDIIFJiBYdxk/+cmNxbL5YL0qWHMiRLBAQLDuMH4ywsKsnmIQLDABwbrdGPdKXyybD9arDroTLLgRgnW7QbAmePQOFsECCcG62ZjqlbpYNh+sF504aidY1TRz5zY3aIYYTii/Nuwh90C1ksGg8/rig51r1kmwbjWme6Utls0H60U/mmMsWOfEZTdolhhMKL0wPwPBWpXfHNvJgdWkCRbBeoTx0B9+NhaszCXP3MYXcOlZ/+Bg3YJzp4u3aHyCdatBsF7D04O1u/wcWiqmgzX/FgnButG41CtlsWw+WJ8YrKNz9RGfvHy5kmzqxZv6Yhm01KW7akDmXJYXvedWu6ynn1O3Ki9syhebp/5KSnXrkvJF0aY+ltNp7Sq7JWLt7SUxoXYDYUKt1x5DLEcm3chNP1ibMA05qp738N4nvXt/dmm4mrqzX13ntNPoZtosK/9JWIWttYc3+zPz96G+pZ1483DVZgPButEgWC/i2cFKm2PA2+rZVD3jw5VwZCtzzcVdNWAng9Ut6+mrcCGoybm3knrVeVYPFloxmke39u6SWFe3AT+hzmuqsBqMXMlgrZr1daPqect7393YziALO6SncmEVmNqpH0450+YhLv9PXDh21QRrOLPyPshgyYerNhsI1o0GwXoRTw7WutxFaC7vi2IfnjDl0+RYHJNqQXsxcUd/PZXBksuEnvl17v2ntS+IWEm4bVd/THpaMZpHt/bu0l7OrdlA9VQXE/Aryv3N58zvQDaSCFaYQNobVc9b3HtxYzuDfcjHtlxWbbV2ivpjN9NmS3Jr9U6YnJm8D9XH8cMV1vZt4Hu4icvB+uqZPYW3DZbf8egvCR/8S5rc37KqL2bN0nZQfal/BkGtH/zllf909lXqreRQNIeeY4eUxDz62xHr6jYwWFN1dRVSfK7uxSFIYtihHidG1fPu3XtxYzsXn4/UNS06dHegWjB+RLqtFfWuX39m4j5UUxo9XO3q2MO60WAP60U8OVjJNuyZBE75NhNP+KI7phI+bcq2HY9FIZ9A3bJpvZCXuvXJj63W0i7p1j7atpjbeE3N6ttT4i7MqLrpwqjBjd0M1mVtT91+3WB98hGZuNP1h+E2B1OafLgCBOtGg2C9iCcHKy+ff/W7hFnzJLoQrGLrj6ckp94TqF02rRfy0mSwsu6pW+vdisTah9ueeLIPJ/DYYHUzOJSvCTd+z2cyWGLOU3eaYH3VRu7qldEH6wODVfhXN+Ho79qlu/wUC1b5MmWT9o9hdcum9UJemgpWpzVPYbGibu2jbY+f7KMJiFlGg3Vh1OBGMZck9f9dCpac8/hOywfoypQI1kMNgvUanh+sU/UuVHM5XKwO+ay74ykrqTg3Xk1PD0uz0TGs1ThYQhvMqb+gvSTWlfWO/4wmsOqOJlUXD1N1EKPaUAzu/fAB27hd+4jJYI3W33+8uqmJ1WaRY1iDhytAsG41CNZreH6w/Ftrx+p5emyOAVVvU+W9d6zS6m243h6WXCb0cNvOv8O1Gb1L2G23ei63Wjundkm39u6SWFe3gcGaqhe5YWQ5aOWf/KN3CZuPYlQbn/69r27sZuCr6M5FL1ip2/m3/VxvXLulYbDEauV9OMkpjR+uAMG62binV0YfrA8Nln81FY4WB8LBmax9+7A7J2gvbm9W0C3r6dWN0+dhFb2PQqsRS/YTly6ehyW81IWfNqpGhsNJ4cSn9VSwxKg2WMN7728UM2heRItghbOvVlVt+vdnKlhyZu19qCYtpjR8uAIE63bjjl4ZfbA+NVjlk2Xts+WyQ/NCZNWeTb5Leme6D95mb5f19Qr/jtmpv5JhsITWIJZ0a+8uiQm1Gxis6ZBWPx65KyOwriawHZ3p3n5sR3Uv78S9b1chZrB37W9WaFZZrn9dXRHj+oN6e5/dzJr7UE26ntLUwxUgWLcbBGuS9m2lx/DUYF3a5lds1Aym7z3BusO4vVdGH6wHBKt9x/pR3/UE69WYvvcE6y7jxlxZfbDuD5Zzjy4WwXo1pu89wbrPuLFXRh+su4Pl3MOLRbBejel7T7DuNubXav42vtzQBcu5xxfL8rMHXg7BwtAYqmANe/WQYhEsEBAsDI3xyGDNKxnBAgHBwtAYmmCNezUZJtd+UEGwQECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgPDBYH3eF2CBaGxmAPC0xAsDA0xgNPayBYcDsEC0NjECwwAcHC0BgP/NEcggW3Q7AwNMYjf/iZg+5wMwQLQ2N81q+XAbMQLAyN8Vm/wA/MQrAwNMZn/YpkMAvBwtAYb/pHKGBpECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNMbsE0cf1hmCBQKChaExbvjRHH6WEB4PwcLQGLf88PNy/1Q9mIVgYWiM234fFn/5GR4MwcLQGAQLTECwMDTGjb8i+QG1IVggIFgYGuPWv5pzf24IFggIFobGeGSw5nWMYIGAYGFoDEWwJns1kSb+CAXcDsHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYzw4WDOOuhMsEBAsDI3xwGD5gexhwW0QLAyN8chghcHqOREsEBAsDI3xwPOwOIYFt0OwMDQGwQITECwMjfHInyW8/EJxcrR+KLw/BAtDY/DbGsAEBAtDYxAsMAHBwtAY/MZRMAHBwtAY/E53MAHBwtAY/NUcMAHBwtAY/F1CMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDTG7NMaZm/74uoett48whMAACAASURBVCZ4AwgWhsa45Uz32ZufXuNjVgPvAcHC0BjKYD38JwkJFvQgWBga48ZfLzN7BhMrfcA64G0gWBga49Zf4Dd7CuO13r8KeB8IFobGIFhgAoKFoTFu/iMUs+cwWu3da4A3gmBhaIzrwZru1f3FIlggIFgYGoNggQkIFobGeGywXGtcnxPBAgHBwtAYV4N1qVfTf/o5fGw/xFd8dQR8EAQLQ2M8MliuIFhwIwQLQ2M8dA+LYMGtECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0xhNOayBYMB+ChaExCBaYgGBhaAyCBSYgWBga47E//MyZ7nAjBAtDY/DrZcAEBAtDYxAsMAHBwtAY/IpkMAHBwtAY/BEKMAHBwtAY/JkvMAHBwtAY/CFVMAHBwtAY/Kl6MAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDDBmwfrxxXP3cgnGAQLTPDWwfpxx/M28hkGwQITvHOwfvzj24pl7o4YMDgPC0zwvsH68RAb01qowZnuYAKC9eJpLdTgZwnhJpLV7hQunHarRDH+2nfN2wZr1Ct9sWzdERsGv60BbqL8JliHC2vdt8OnBmuiV+pimbojRgx+HxbchHNptWOVpAQrAsF6ebCmevWAYhGsRePcxh3Lz8fyM8G6yGSvtMWydEesGAQLbsK53O3Kzzu3r78ZdqlLduFSvnIu2VQXM+eyvGiCJcY0N9QQrFdOa7nGY/9qzhwI1qJx7uxW5eeVO1XfC6vwXZGVl7bVN4gv1q66uKuD1Y3pbqghWK+c1nKNx/7l5zkQrEVTfv1T/yV0SdWi3GXn4py53N+0L4pqvyvxLxv3Lq2CJcZ0N5Tfg4Hv35ILwfrqaS0XggU3UX79N+5QHNy62Xk6lx+rva5mQHjhKK6KMd0NNexhvXJayzUeGyw3+BxdsWIMmKX8+u/dtnz5t2/SJL4tTvk2C5c2zq2Ox3q8HNPdUEOwXjmt5RrXgnW5VxPFahbp3jZSjAGzlF/jk8uKzJ3Gwcq6749tUl5ITqNgdTfUEKxXTmu5xiOD5Zq//Mwe1vvjv/yJO7ukffHX3rR26S4/NQvyTdocw+p/y9Q31LxpsDgP68HGQ/eweEn4Ofgv/9pt/Onu9TGsXN5UnMT3RxOr1fDAlRhDsF46rcUaBAtuwn/59y68IRi+E/YuOfqzFcIB9UNxrI5hpdUbhmkzvh3T3VDzrsHiZwkJFhjAf/nLvSh3avaTqgNX/qjUpv7+OFRJqy6Jg1t+THdDzdsGi9/WQLDg66lPs0qK9oXdLnVuHY6ir53LDnk4wyGc0H5oB3Vj2htqCNaLp7VQg9MawATvGyx+4yjBgrfjnYPF73R/nEGwwARvHSz+as7DjMf+8DPBght582C9aCPvb/DrZcAEBAtDYxAsMAHBwtAY/IpkMAHBwtAY/BEKMAHBwtAY/JkvMAHBwtAY/CFVMAHBwtAY/Kl6MAHBwtAYM4L1YAgWCAgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDM7DAhMQLAyNwZnuYAKChaExdMF6ws8+EyyQECwMjcFvawATECwMjXHrL/CbPYXxau9fBbwPBAtDY/AbR8EEBAtDYzz2j1DMgWCBgGBhaIzH/pmvORAsEBAsDI1BsMAEBAtDY1wL1uVe3VssggUCgoWhMQgWmIBgYWiMxwbLtc71OREsEBAsDI1xJVixXo2rVC3pPsRXfXUEfBAEC0NjPDJYriBYcCMEC0NjPHQPS0SIYMEsCBaGxiBYYAKChaExnhQsDrrDPAgWhsYgWGACgoWhMZ5xWoMuRgQLBAQLQ2M8JViqFhEsEBAsDI3xlBNHVXMiWCAgWBga47E//Ow64+qcCBYICBaGxuC3NYAJCBaGxuAX+IEJCBaGxuBXJIMJCBaGxuCPUIAJCBaGxuDPfIEJCBaGxuAPqYIJCBaGxuBP1YMJCBaGxtAGq1CfX6Ve3cPWBG8AwcLQGDOC9WAIFggIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJCBaGxiBYYAKChaExCBaYgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJCBaGxiBYYAKChaExCBaYgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJCBaGxiBYYAKChaExCBaYgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJ3jxYP6p47kY+wSBYYIK3DtaPOp63kc8wCBaY4J2D9aMf3VYsc3fEgEGwwATvG6wfDbExrYUaBAtMQLBePK2FGncEy7nq/wl2iUK/fcvvxXGduHUeLl54OC/SjVebmoGRr+wV8llzkbxtsEa90hfL1h2xYTwnWKpnxe1bfis2LpCeiuUHK3Wz5iJ512BN9EpdLFN3xIhxZ7Dm39SNuX3L78TWJeVuybn8dLonWA/l5tU6gjWEYL0uWOU33splp/b6YeVcsvGXTplbtf8Oi+/SbeLSnb+s+J4lWJ5TCFXJ2q0J1hRLe0b1meyVtliW7ogVIx6sdVme5FxfzavXLmWxzkn5eTUOVvXqZkew9GzctrpwXoXSlwuSaskudcnOXygXbsPCTXjwq5vS5qZ6HdUX4rQa2TV55lyWd0pSrmrg5O2/R91Xtltha1RUM62m04qu+sI39+LgG+y/b8KRrbU7tCPPLq3udP25IFivndZyjXiwsnOR1U8Sf4BiXxTHKkxZcc7GwSq/wcvv0oSXhHoydxTXwj8DPvlFES6Uj7NfuPUXfXKqYmXipqL6atQPv/+HJBSwsyt2rv63pFLCGtZ9Z9v+eySC1a6wM5qZbpstdGIbrPpeJGH4uvoOKr8vupGrsl4l+ybXBOu101quEQ/W0b9maf8RLE75NmQqLcNU3jAK1qx3uwiWp/9IhX8idv4Rz/2l8h+FvF1YfUz8szw5FsfE//PR/OvRfCEm7IrEfyn3/iY/MK/X0HOcX+E+zKf7ynYrbI1mpmISPVFoW3+LH1pUbepG5tXO19p/IxXfBr5/Sy4E66untVyuHMPqPaWqf9kH+1TyWp7MeLeLYHmGwTrUy1bOvxI/u1W38NTe5DuU1/tVVa/qL8SE3aw4Fxts1tBzxHy6r+xhbMgV5nILRfetUGsnP7dDub919N88JzkyDTMU/xiyh/XKaS3XmBGstUt3+SkWrPIVY+qSA8HSMwxW89E19B9fccJBvQtU1WH02ry1Kzbly7TjcXIN3cqbHeiJr6wwevOeEOXHrMzSxh3LnatT1dV25M6/GDx0rwgJ1kuntVwjHqzwyi9rrxb+X+3pl4Sn5nt5J76/oxAsz6o9hpWfbwmWS6q9lCvB8u/fuvbMielgtTvQc4PVF3u73GWRkrRIm1eH3cizf524dd1b0ATrldNarnHloLs/ELJtrx7qQ+3b6mh8/Y3sD2RUy5NyxJGD7nPYNg/voTnA1CtEc/VisA5HVx8oumC35Ju02cRksLod6JnBGoi9jy49uE25k3UOLwHFyHJZ7kPW8qbB4jysBxtXguWqI6bhu2/T/qvdP60hLN92I7Z+8fWfzSFYnvY8rKx9C08cNCqaq+Ob8urwVjjztBj2RdiSZlDvGJa8obgUrIljWIeiPnY+EHsfN25dmuWwcJBd7osfXXYUrwgJ1muntVjjykvCzK1P1cXC/wPpskP1TbcSJ476U3S27aVw2s6OYGlZhzPd/flOhXyqh7cCy8dxNa7A4F3C8gX6dtgXYVek1ftzU+8SNo5P0PHSMayL7xLmA/HUm2q53+hceKnrqnPA2pF+Sol4Rfi2weJnCV990D2K5rXfJfVm873IRj9LKA4MdT+vM7qpPQ/r6JsweTwqaYOwrzZy6N3cd5od6MNUsIRRUe19hyIKMXXdAYHqY3gZWtrhHzAxMuyuiVeE7xssfluDoWCdCNb97MsX19k+XJRP9V357F8PKtbclPTOdN/K3d2hXRPOdO/OYCh3haudZeGEHej2leZwha1RNDNd1ZMQ4iEdBmsbzhrd1mcfdyPD2zfiFSHBevW0FmrcFaytq87/uwmC9dWIM+FnG3f8S1WTO/mK8I2DxW8cNROsdfVC5jYI1pcR3lg8r9ofurrBuD9Y4V2GjncOFr/T/XEGv3H0A6l/pk/xWxYvGvcGy7nB/t1bB4u/mvMwg2B9IrvMuVS/fzVh3BuspHsHs+LNg/Wijby/QbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjRIL1TYzZUxhBsEBAsDA0RiRYf02M2VMYQbBAQLAwNEYkWD8nxuwpjCBYICBYGBojEqyfG2P2FEYQLBAQLAyNEQnWXxtj9hRGECwQECwMjREJ1l8XY/YURhAsEBAsDI0RCdbPizF7CiMIFggIFobGiATrr48xewojCBYICBaGxogE62+IMXsKIwgWCAgWhsaIBOvnxxiMHden+QXgvb+OF1fggyFYGBojEqy/MUZ/6LhJrv7g2iuTIwAqCBaGxogE62+K0RvpxvUhWDALgoWhMSLB+gUx5MC2S/LPQPVumFo9wQIBwcLQGFd/W8MvHDIeUnep1yaCBbMgWBgaIxKsXxRDjHPFlWBNH3UnWCAgWBgaIxKsvzlGN6ztVO9PA8ul7GHBVQgWhsaIBOsXx+iGdX/1vLs+tbs1hGCBgGBhaIxIsP6WGIOx7UtCuYBjWKCGYGFojEiwfkmMwVgOusN9ECwMjREJ1i+NMRg7PqmdM91hDgQLQ2NEgvW3xpg9hREECwQEC0NjRIL1t8WYPYURBAsEBAtDY0SC9ctizJ7CCIIFAoKFoTEiwfrbY8yewgiCBQKChaExIsH6O2LMnsIIggUCgoWhMSLB+jtjzJ7CCIIFAoKFoTEiwfq7YsyewgiCBQKChaExIsH6u2PMnsIIggUCgoWhMSLB+uUxZk9hBMECAcHC0BiRYP2KGLOnMIJggYBgYWiMSLB+ZYzZUxhBsEBAsDA0RiRYvyrG7CmMIFggIFgYGiMSrF8dY/YURhAsEBAsDI0RCdaviTF7CiMIFggIFobGiATr74kxewojCBYICBaGxogE69fGmD2FEQQLBAQLQ2NEgvX3xpg9hREECwQEC0NjRIL198WYPYURBAsEBAtDY0SC9etizJ7CCIIFAoKFoTEiwfr7Y8yewgiCBQKChaExIsH69TFmT2EEwQIBwcLQGJFg/QMx5MDJP4zDX82BORAsDI0RCdY/GEOMm/zTg/xdQpgFwcLQGJFg/UMxxDiCBfdDsDA0RiRY/3CM4eDwp5/Fqz+CBbMgWBgaIxKsin9kyPSwYZsIFsyCYGFojEiw/tEYvZHVntXFYE0fdSdYICBYGBojEqx/LMZwsKteEbphsELMCBZcg2BhaIxIsH5DjOFg1xaoqtZ4d2vCAGggWBgaIxKsfzyGGNc7XDW5kGDBNQgWhsaIBOufiCHGTbaJYMEsCBaGxogE65+MIQdOntTOme4wB4KFoTEiwfqnYsyewgiCBQKChaExIsH6p2PMnsIIggUCgoWhMSLB+mdizJ7CCIIFAoKFoTEiwfpnY8yewgiCBQKChaExIsH652LMnsIIggUCgoWhMSLB+udjzJ7CCIIFAoKFoTEiwfqNMWZPYQTBAgHBwtAYkWD9CzFmT2EEwQIBwcLQGJFg/aYYs6cwgmCBgGBhaIxIsH5zjNlTGEGwQECwMDRGJFj/YozZUxhBsEBAsDA0RiRYvyXG7CmMIFggIFgYGiMSrN8aY/YURhAsEBAsDI0RCdZvizF7CiMIFggIFobGiATrt8eYPYURBAsEBAtDY0SC9TtizJ7CCIIFAoKFoTEiwfqdMWZPYQTBAgHBwtAYkWD9rhizpzCCYIGAYGFojEiwfneM2VMYQbBAQLAwNEYkWP9SjNlTGEGwQECwMDRGJFj/cozZUxhBsEBAsDA0RiRY/0qM2VMYQbBAQLAwNEYkWL8nhhw4+Ze8+DNfMAeChaExIsH6V2OIcZN/K5U/pAqzIFgYGiMSrH89hhhHsOB+CBaGxogE61+LMRzs8yNf/REsmAXBwtAYkWBV/BtDpocN20SwYBYEC0NjRIL1b8YYjHVFJFjTR90JFggIFobGiATr34oxGNu8InTDYPml7GHBVb4uWD8EnruN2xSMMZFg/dsx+kPlzlVVrfHu1hCCBYKvCtYPLc/bxq0KxphIsH5vjN5IJz7KRRzDAi1fFKwffritWATri4xIsP6dGHKgE5846A438TXB+uGHG4tFsL7IiATr340hxrXHrnontXOm+0WcYPCOxE77qHQHCy+tbJe6ZHO+vpbRl6ddeF47t6k/Ksl7c4stHPIlwfrhh1uLRbC+yIgE69+LMXsKIwjWOFjHa09rsY4rK9uEJUm8WPFgrcoVbOuPOlLXm1tk4YivCNawVzOKRbC+yIgE6/fFmD2FEZ8brEDzBJZP5GMyP1gXVnZ067PfYVtfXctksOoLp/bjLbOKLRxBsDA0RiRY/36M2VMYQbB6nwufl+xhwVq5iWGTa4kFS7GKyKxiC0d8QbDGvdIXi2B9kREJ1u+PMXsKIwhW83njkvCay20GT+68fDmWbKpRp1U9qtgko4ETK+vfUHtZtbPkD2/tmpsHQzbNwt5rzZ5zTt2qv6SeXD02jG/nLhd6J+07DQQLQ2NEgvUfxJg9hREEq/7sDxI5/yw+DvZGtlUtqmd94upjSZm/sLoUrHZlgbPL2jFZe0wrjAm3DILVrXoULOmswpzkknpyok3d3GWwsrHTQLAwNEYkWP9hjNlTGEGw6s/ZuXwxmPaX1lf2RbGv34BtRu1dchwd7JpemX+RmTcX9/6mtS9I7i+dM39TP1hi1a77kapqf0k62XliSdhqZ/Tm3ixsNrDvz/TbwPcvZypYr58FzCISrP8oxpzn5jQEq/586F2bHtiNWoVL+aVgyZUVp2TVDgne2SX+kg/O2b+s6wdLrHoYrJ5zKKaWdOsSv7Ojv5pV6Gfud7EGM2UPC0NnRIL1n8SYPYURBEt8vhCsU77NBk96N3U0fXpl5yQbDxEnQgyDJVY9DNbAmVoyDFZ87qMD+gQLQ2NEgvUfx5g9hREES3yeDlbWHvK+KVhZOjHkVcG6MncLweK0hgUakWD9pzFmT2EEwRKfJ4O1dukuP90arFOanSaG9NQZwRquaLSkn6FrcydYGDcZkWD9ZzFmT2EEwRKfJ4MVrgyf9NVxoMPVYOXiDUJPJo5h5dLr1iRWPQzWwJlaMrGvNT33vDl69uXB4kdzlmdEgvVdjNlTGEGwxOcLwToUx+FxoDz+LmHz8TTolT8r9Vxs/LuE4b268vrooHt++V3CgTO1pNJO7SUx92Zh713Cwd3lh58xNEYkWH8gxuwpjCBY4vNksDb1UaLDYGenZH0tWGtxzmdFdx5WdXgpOQ2DJVY9DNbAmVriP6bO78L5S2Lu3cLeeViDu8uvl8HQGJFg/RcxZk9hBMESnyeD5auTHUavoLbRM92LJkPDYPmGrNoz3d262xfqhmzFme6DqfWcqSX+4yFt29TNXSwsdkl7pvvg7vIL/DA0RiRY/3mM2VMY8eHBgj78imQMjREJ1h+MMXsKIwgWCPgjFBgaIxKsPxRj9hRGECwQECwMjREJ1h+JMXsKIwgWCAgWhsaIBOsPx5g9hREECwQEC0NjRIL1X8aYPYURBAsEBAtDY0SC9V/HmD2FEQQLBAQLQ2NEgvVfxegPnYhPcw5Q/1yguAOfC8HC0BiRYP03MXojJ5Lk6g+uvTI5AqCCYGFojEiw/tsYcqCbiA/BglkQLAyNEQnWfxejP7SKj3z1R7BgFgQLQ2NEglXxPwyZGuT6HwqCBTMhWBgaIxKs/z5Gf2g8WNNH3QkWCAgWhsaIBOt/jNEf2r4f6IbB8kvZw4KrECwMjREJ1h+N0R8qd67q3/MtlhAsuArBwtAYkWD9TzH6Q8dHqjiGBbMgWBgaIxKsPxajP5SD7nAnBAtDY0SC9cdj9IeOT2vgTHeYBcHC0BiRYP3PMWZPYQTBAgHBwtAYkWD9iRizpzCCYIGAYGFojEiw/mSM2VMYQbBAQLAwNEYkWP9LjNlTGEGwQECwMDRGJFh/KsbsKYwgWCAgWBgaIxKs/zXG7CmMIFggIFgYGiMSrD8dY/YURhAsEBAsDI0RCdb/FmP2FEYQLBAQLAyNEQnW/x5j9hRGECwQECwMjREJ1p+JMXsKIwgWCAgWhsaIBOvPxZg9hREECwQEC0NjRIL1Z2PMnsIIggUCgoWhMSLB+vMxZk9hBMECAcHC0BiRYP0QY/YURhAsEBAsDI0RCdZfiDF7CiMIFggIFobGiATrL8aYPYURBAsEBAtDY0SC9ZdjzJ7CCIIFAoKFoTEiwfpLMWZPYQTBAgHBwtAYkWD9nzFmT2EEwQIBwcLQGJFg/R8xZk9hBMECAcHC0BiRYP1fMWZPYQTBAgHBwtAYkWD91RizpzCCYIGAYGFojEiw/kqM2VMYQbBAQLAwNEYkWP9PjNlTGEGwQECwMDRGJFj/d4zZUxhBsEBAsDA0RiRY/1+M2VMYQbBAQLAwNEYkWP9vjNlTGEGwQECwMDRGJFhPhmCBgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJCBaGxiBYYAKChaExCBaYgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJCBaGxiBYYAKChaExCBaYgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpiAYGFoDIIFJiBYGBqDYIEJCBaGxiBYYAKChaExCBaYgGBhaAyCBSYgWBgag2CBCQgWhsYgWGACgoWhMQgWmIBgYWgMggUmIFgYGoNggQkIFobGIFhgAoKFoTEIFpjg64L1XeC527hNwRhDsMAEXxWs71qet41bFYwxBAtM8EXB+u6724pFsL7IIFhggq8J1nff3VgsgvVFBsFaFs7JC9U119AbuZt+gJ3icfdjNOPG5NpNjPiSYH333a3FIlhfZBCsZaEO1vFCNp4arNRpNzHiK4I17NWMYhGsLzII1rKYDtZ43DG5bRepv5HbRIL1IAVjDMFaFspg7VxGsK4x7pW+WATriwyCtSyUwXKbwdK8DFiWt4M3ST3CudPKJdswZOVcsmlWOLixM9rNb+rRnVi/Lq1u2xYHtw43u3Bka+0O7cizS4PZfC4IFobOIFjLQhms42DprjrItasXZ/7KuopL4i+XUdpWQzaFDFZzozCazYfxWSHFNliramNJGL52VQUTMXJV1qtk77bNCgkWhsYgWMvCdRTDg+7DkeJK4o4+D2m1OHfJsT7KVTbnXOYs3LD3Q5rV9m8URrP2asF+JApt62/xQ4uqTd3IvNr5WrtT+fHbwPcvZypYr58FzIJgLYsbg+Wql2X14lW4lldNOvTGDoJ1GBtyhblbjUShnfwe2KHc3zr6XbSTHJm6s7/YviJkDwtDZRCsZaF9l3CwdFO+TDsem8X1Td3F6uMp32aDYE0YvbVPiPJjVmZp447lzlVolxi58y8GD90rQoKFoTII1rKIB0vuafUztvVHo5JTLFhZb8dtRrCy0R5f8zEvi5SkRdq8OuxGnv3rxG2718VpDRg6g2Ati1uDVcZjkzbHsCaDtXbpLj/dEKyB2Pvo0oPblDtZ5/ASUIwsl+U+ZC0EC0NjEKxlceNLwm7R6BiWvKG4FKyJY1iHoj52PhB7HzduXZrlsHCQXYwsji47ileE/GgOhsogWMvixmCl1ftzU+8SNmN9go6XjmFdfJcwH4in/qwO5Q7fOez5VeeAtSP9lBLxipAffsZQGQRrWdwYrH31YvHQP+rUa9LG9cZMH+ASwQpL/JuEQkzDKQxyVuFlaGn7MxvkyLC7Jl4R8utlMFQGwVoWt74kDGe6d2cwbJLy2qBJaz8inKgwEazOaNe+cukuXO7EQzoM1jacNVp9lCP9YXf5ipBf4IehMgjW51KdaXCbMR3JOeROviLkVyRjqAyC9YGEM87Pq3q35ybj/mBlbiev8kcoMDQGwfpA6p/pS+4w7g1W/XOIHQQLQ2MQrE9klzmX6vevJox7g5V0P9RTQbAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0xnzojQAAD+9JREFUBsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwfoAXCBZn+5dUV6t7f4ZjbnwjfhNydxV2XwOGp3W0gyC9QG4muTOYqWuWtsj5jRk8hvxm5p5q7L5HDQ6raUZBOsDqBJzztzmEet5DlPfiN98c1OxbD4HjU5raQbB+gDq0Jxd8pD1PAWChaExCNYH0IQmfM5X5WvDTXX1tHLJNty0SVx2CsvOqVuVF3apS3bhplYILyubtTVCQ3s9vn6x4nZTgYlvxG++ua1YNp+DRqe1NINgfQByD2tbHc6qApT4i74oWTjEdfbLVuHGVRiVFVKQwWqFmvb6lfWLFTebqiBYGBqDYH0AVbBOWVWdfVHsw5KyG+di51J/vby0rm7OfFZy/+mcuXwgNGvrhAq5guj6eyvOquB9G/h+hAzW+Fb4SAjWB9C+S3juloQPh/riyl8KO2DVsnKBH3ruXrENgtUJFcPrF9cvVlxvqoY9LAyNQbA+gP55WKd8mw360x1Nry81iXNXhZ6mWX+74v46OOiOoTEI1gfQS0PWlkgZrLgw2sK19RMsjDsMgvUByDSsXbrLT9eDpRWGW9CtfzgrThzF0BkE6wOQaQiXh0HJxDGsMGrlcqVQMVjB5eHDFbfwozkYGoNgfQD9YB2K4/AY086/Ybep3uQLo/YuOfrFq4FwGgkVcgXR9fdWLOfIDz9jaAyC9QHINGzqY0gHGRR5HlY1LGt/+FAIqWv3oS6eh3Vl/WLFBAtjvkGwPoBeGtbOZYe828WpT1x3bnWSQ3dlnaq3FTvhkHavGhuhob0eX79YMcHCmG8QLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAsCburr0V84OWTmGi/ecOEb8ZuSWVstvvoZ9aUbeX+DYEHAZLC+qZm1XaPPQaPTWppBsCCgqNHMYM1j6hvxm29uKpbN56DRaS3NIFgQIFhPNoxOa2kGwYKArNEudcmuXZhnzmV5dW3jkm11aRsubZzb+IH5yrlkUymnVTtI3tBbj7hWM/GN+M03txXL5nPQ6LSWZhAsCIhglY0pyeqFu3DN7fy1VXtp6y/56IRibasxm6Ak/uK2ksUNcj3yWg3BwtAYBAsCXbByl52Lc+byamHijkWxd6m/Vt6wE5eqj4m/vvdjXNEf1LtBrkdeK78HA9+PkMEa3wofCcGCQBeslTuXH89u1TQnb4ccimZZdenUE+tgdYMGN+Tianethj0sDI1BsCAg81JTLdyUrwSPx25I1yL58ZRvs7pL/UHtDf31dNdqOOiOoTEIFgQuBqvY+oNSySkarKwxhoO6Gwbraa/VECwMjUGwIDB4Ade7mG/S5tjTdLDWLt3lp4lgiRuG62mu1XDiKIbGIFgQkMew8vHCfqaGwQqXpoIlbhiup79+fjQHQ2UQLAh07di75OjPO6gPuqfVG33RPSx/pP04dQxL3NBfT3ethh9+xtAYBAsC3YGr+rhTc7RpXy0/xIK1cRcGiRv66+mu1RAsDI1BsCAgguXPdHfr9pSFcE56c67ChYPuaz8kb3bKxKDuhsF62ms1BAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwAcHC0BgEC0xAsDA0BsECExAsDI1BsMAEBAtDYxAsMAHBwtAYBAtMQLAwNAbBAhMQLAyNQbDABAQLQ2MQLDABwcLQGAQLTECwMDQGwQITECwMjUGwwAQEC0NjECwwwbcAGr7sO5RggYZXfIe+5FnwLnfkXbYxF4IFGt7m+fEud+RdtjEXggUa3ub58S535F22MReCBQCLgWABwGIgWACwGAgWACwGggUAi4FgAcBiIFgwwSZxyebcXT+vnVsfw0VX8fhteA7u8m2P3sjT7ohc8aPuSGQbz/uCHP1X/TR921dBsGBMFp4CabcgCQt8sY6Pen6MtlFyTtzF2x69kafdEbniR92RyDae9wXJw4LkPHXbl0GwYMTBJcfimLhDs2Dj1v7DqvDPj9VztuFZVc+7ydsevZGn3RGx4kfdkdg2nvcFScoF55XbPPALcj8EC0ZsXF5+3LttsyBx/p/Z8ETfdUsfu41wrWrJ1G0P38jT7ohY8aPuSGwbT7sfe5+q4uySB35B7odgwYiV8wcuRv90+2/d8vmxe9Y2Ti6rWnJh+4/dyNPuiFjxo+5IbBtPux9rd7x429dBsGCEc/JTwyY8M1YuX7tk84xtZO7UHEae2v6DN/K0OyJW/Kg7EtvG0+5H6opt4tbnqdu+DgNTAGtMfX+Wr6Tq50cge/w2tm5fPD1Y3UaedkfEip8YrHYbT7sfzoVVJ1O3fR0GpgDWmPr+3K2ScAjDlc/44ry5+3XIaBvhBcezg9XbyJPuiFjx04LV28bT7oc/6L72X3WCBZa58P257p4U57vf4h6/AvHvnz87WGIjFU+4I2LFTwuW2Mbo4qO2UZ3Gcnrk/XgABqYA1kguPT+S9vLd37vDbazDG1HV1Qvbf+xGah5+R+SKH3VHYtsYX3zQNkSlHvYFuR8DUwBrVG8KnUZvCj3y+THchmu5vP2HbqRd9OBtyBU/6o58xRekXNCu+GFfkPshWDBiG3ZEcte+9VSdhxVeHTQX7/3eHW5DtmS0/Wds5Fl3RK74UXckto2n3Y9qwckfzn/YF+R+CBaMmD7T/bzyx7A2/rv2XJ1J+NBtBF5xpnvRnJ36pDsiVvy0M93FNp52P8p/n87+oPueM93BOGn3TnlzVKlZcK4u3v9v7Wgb4pK47WkbedodkSt+1B2JbON5X5Btt+BhX5C7IVgw5hx+OD9crJ/n5YJ0196WPuDk6vE2ukvitudu5Cl35Nx/sB5xR65t4zlfkDxrFjzsC3I3BAsAFgPBAoDFQLAAYDEQLABYDAQLABYDwQKAxUCwAGAxECwAWAwECwAWA8GCT+S8879Oc6U8Qbz+iels9LN0eXXrY+cGEXis4QPJk7pByUkzvP0lD4NipfL3DcIr4LGGzyN3bu3bc1iJ30kYofmByuGP/5Kql8MjDp9H0v4ylrXqd6E3YRoGimC9HB5x+Dj23W+7O23Cq7xd6pJQLudO5V5X9QdD24WDYOUrV/3qgvpXAVZLy9GP+J0JEIdgwcexGh6LWok/zBWObm17C/svCatfE+V/AZUMVmbmN0a9NwQLPo7hK7ncZefinPnXic5f3PnfBS0Wdgfdj0X9d7X2YR3dH/nZ17+Tc//yO/NhECz4ONq/B9P+0Qv/a9HP1d8sPBTNH15oF7anNRwH6+iCtap/6zm7WE+GYMHHMQyW+NMUXYLEwmppmjRH6k/5NhsEy9Bf7ntveIDh45DHsPTBOjhXnbSV9ZcTrFfCAwwfx97/EaAaUZvm6mhhfXlVvbm4dukuPxGsr4EHGD6P7jysc3cAKjA8KiWXHpuD7oX/G1jTx7AM/KnR94ZgweeRO7cKZ7pvnD/VPbzFV+yqg+7+9vZ9v2phs+O0qq8cimNzDOskR/Mu4fMhWPCBHJqfJaxeHGbtzxV2wRIL2z8MFnaxNt3PFaahd5yH9UIIFnwk+1XZrGxb/+zzrkzPuktTe+56tbA9NLUJu1hr/3sbwqu/Q9oGq9g95q8DQhyCBQCLgWABwGIgWACwGAgWACwGggUAi4FgAcBiIFgAsBgIFgAsBoIFAIuBYAHAYiBYALAYCBYALIb/H0V4scHHrN3YAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#label_format=150表示标签长度为150，即每行显示150个字符，避免自动换行\n",
    "p1 <- barplot(GO, split=\"ONTOLOGY\", label_format=150)+facet_grid(ONTOLOGY~., scale=\"free\")#柱状图\n",
    "p2 <- barplot(KEGG,showCategory = 30,title = 'KEGG Pathway', label_format=150)\n",
    "p3 <- dotplot(GO, split=\"ONTOLOGY\", label_format=150)+facet_grid(ONTOLOGY~., scale=\"free\")#点状图\n",
    "p4 <- dotplot(KEGG, label_format=150)\n",
    "p1\n",
    "p2\n",
    "p3\n",
    "p4\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 富集基因——所在功能集/通路集__关联网络图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in cnetplot.enrichResult(x, ...):\n",
      "\"Use 'color.params = list(edge = your_value)' instead of 'colorEdge'.\n",
      " The colorEdge parameter will be removed in the next version.\"\n",
      "Warning message in cnetplot.enrichResult(x, ...):\n",
      "\"Use 'color.params = list(edge = your_value)' instead of 'colorEdge'.\n",
      " The colorEdge parameter will be removed in the next version.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p5 <- enrichplot::cnetplot(GO, circular = FALSE, colorEdge = FALSE,\n",
    "         shadowtext = \"all\",\n",
    "         color.params = list(foldChange = NULL, edge = FALSE, category = \"#e13a38\", gene = \"#2fa7f3\"))\n",
    "p6 <- enrichplot::cnetplot(KEGG, circular = FALSE, colorEdge = FALSE,\n",
    "         shadowtext = \"all\",\n",
    "         color.params = list(foldChange = NULL, edge = FALSE, category = \"#e13a38\", gene = \"#2fa7f3\"))\n",
    "p5\n",
    "p6"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 富集基因——所在功能集/通路集__关联热图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p7 <- enrichplot::heatplot(GO,showCategory = 50, label_format=150)#基因-通路关联热图\n",
    "p8 <- enrichplot::heatplot(KEGG,showCategory = 50, label_format=150)\n",
    "p7\n",
    "p8"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 富集功能集/通路集之间__关联网络图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in emapplot.enrichResult(x, showCategory = showCategory, ...):\n",
      "\"Use 'layout.params = list(layout = your_value)' instead of 'layout'.\n",
      " The layout parameter will be removed in the next version.\"\n",
      "Warning message in emapplot.enrichResult(x, showCategory = showCategory, ...):\n",
      "\"Use 'cex.params = list(category_label = your_value)' instead of 'cex_label_category'.\n",
      " The cex_label_category parameter will be removed in the next version.\"\n",
      "Warning message in emapplot.enrichResult(x, showCategory = showCategory, ...):\n",
      "\"Use 'cex.params = list(line = your_value)' instead of 'cex_line'.\n",
      " The cex_line parameter will be removed in the next version.\"\n",
      "Warning message in emapplot.enrichResult(x, showCategory = showCategory, ...):\n",
      "\"Use 'layout.params = list(layout = your_value)' instead of 'layout'.\n",
      " The layout parameter will be removed in the next version.\"\n",
      "Warning message in emapplot.enrichResult(x, showCategory = showCategory, ...):\n",
      "\"Use 'cex.params = list(category_label = your_value)' instead of 'cex_label_category'.\n",
      " The cex_label_category parameter will be removed in the next version.\"\n",
      "Warning message in emapplot.enrichResult(x, showCategory = showCategory, ...):\n",
      "\"Use 'cex.params = list(line = your_value)' instead of 'cex_line'.\n",
      " The cex_line parameter will be removed in the next version.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GO2 <- pairwise_termsim(GO)\n",
    "KEGG2 <- pairwise_termsim(KEGG)\n",
    "p9 <- enrichplot::emapplot(GO2,showCategory = 15, color = \"p.adjust\", layout = \"kk\", cex_label_category = 1, cex_line = 0.3)#通路间关联网络图\n",
    "p10 <- enrichplot::emapplot(KEGG2,showCategory = 15, color = \"p.adjust\", layout = \"kk\", cex_label_category = 1, cex_line = 0.3)\n",
    "p9\n",
    "p10"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GO有向无环图（Directed Acyclic Graph，简称DAG）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GO_BP<-enrichGO( gene$ENTREZID,#GO富集分析BP模块\n",
    "                 OrgDb = GO_database,\n",
    "                 keyType = \"ENTREZID\",\n",
    "                 ont = \"BP\",\n",
    "                 pvalueCutoff = 0.05,\n",
    "                 pAdjustMethod = \"BH\",\n",
    "                 qvalueCutoff = 0.05,\n",
    "                 minGSSize = 10,\n",
    "                 maxGSSize = 500,\n",
    "                 readable = T)\n",
    "p11 <- plotGOgraph(GO_BP)#GO-BP功能网络图\n",
    "GO_CC<-enrichGO( gene$ENTREZID,#GO富集分析CC模块\n",
    "                 OrgDb = GO_database,\n",
    "                 keyType = \"ENTREZID\",\n",
    "                 ont = \"CC\",\n",
    "                 pvalueCutoff = 0.05,\n",
    "                 pAdjustMethod = \"BH\",\n",
    "                 qvalueCutoff = 0.05,\n",
    "                 minGSSize = 10,\n",
    "                 maxGSSize = 500,\n",
    "                 readable = T)\n",
    "p12 <- plotGOgraph(GO_CC)#GO-CC功能网络图\n",
    "GO_MF<-enrichGO( gene$ENTREZID,#GO富集分析MF模块\n",
    "                 OrgDb = GO_database,\n",
    "                 keyType = \"ENTREZID\",\n",
    "                 ont = \"MF\",\n",
    "                 pvalueCutoff = 0.05,\n",
    "                 pAdjustMethod = \"BH\",\n",
    "                 qvalueCutoff = 0.05,\n",
    "                 minGSSize = 10,\n",
    "                 maxGSSize = 500,\n",
    "                 readable = T)\n",
    "p13 <- plotGOgraph(GO_MF)#GO-MF功能网络图\n",
    "p11\n",
    "p12\n",
    "p13  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导出图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir <- \"07_figure\"\n",
    "if (!dir.exists(output_dir)) {\n",
    "  dir.create(output_dir)\n",
    "}\n",
    "\n",
    "# 设置画布宽度和高度（以英寸为单位）\n",
    "width <- 10\n",
    "height <- 10\n",
    "\n",
    "# 保存GO柱状图\n",
    "ggsave(file.path(output_dir, \"GO_barplot.svg\"), p1, device = \"svg\", width = width, height = height)\n",
    "\n",
    "# 保存KEGG柱状图\n",
    "ggsave(file.path(output_dir, \"KEGG_barplot.svg\"), p2, device = \"svg\", width = width, height = height)\n",
    "\n",
    "# 保存GO点状图\n",
    "ggsave(file.path(output_dir, \"GO_dotplot.svg\"), p3, device = \"svg\", width = width, height = height)\n",
    "\n",
    "# 保存KEGG点状图\n",
    "ggsave(file.path(output_dir, \"KEGG_dotplot.svg\"), p4, device = \"svg\", width = width, height = height)\n",
    "\n",
    "ggsave(file.path(output_dir, \"GO_cnetplot.svg\"), p5, device = \"svg\", width = width, height = height)\n",
    "\n",
    "ggsave(file.path(output_dir, \"KEGG_cnetplot.svg\"), p6, device = \"svg\", width = width, height = height)\n",
    "\n",
    "ggsave(file.path(output_dir, \"GO_heatplot.svg\"), p7, device = \"svg\", width = width, height = height)\n",
    "\n",
    "ggsave(file.path(output_dir, \"KEGG_heatplot.svg\"), p8, device = \"svg\", width = width, height = height)\n",
    "\n",
    "ggsave(file.path(output_dir, \"GO_emapplot.svg\"), p9, device = \"svg\", width = width, height = height)\n",
    "\n",
    "ggsave(file.path(output_dir, \"KEGG_emapplot.svg\"), p10, device = \"svg\", width = width, height = height)\n"
   ]
  }
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