07-rdkit/03-rdkit可视化.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# rdkit 可视化\n",
    "## 单个展示\n",
    "从`mol`对象到图片：`MolToImage(mol, size, kekulize, wedgeBonds, fitImage, …)`              \n",
    "`mol`：mol对象             \n",
    "`size`：图片尺寸，默认(300, 300)             \n",
    "`kekulize`：是否展示kekule形式，默认True（True：芳香系统用实线表示，False：虚线表示）        \n",
    "`wedgeBonds`：是否展示楔形键，即立体构型，默认True            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem import AllChem\n",
    "from matplotlib.colors import ColorConverter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMolToImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mkekulize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mwedgeBonds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mfitImage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mcanvas\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Returns a PIL image containing a drawing of the molecule\n",
      "\n",
      "ARGUMENTS:\n",
      "\n",
      "  - kekulize: run kekulization routine on input `mol` (default True)\n",
      "\n",
      "  - size: final image size, in pixel (default (300,300))\n",
      "\n",
      "  - wedgeBonds: draw wedge (stereo) bonds (default True)\n",
      "\n",
      "  - highlightAtoms: list of atoms to highlight (default [])\n",
      "\n",
      "  - highlightBonds: list of bonds to highlight (default [])\n",
      "\n",
      "  - highlightColor: RGB color as tuple (default [1, 0, 0])\n",
      "\n",
      "NOTE:\n",
      "\n",
      "      use 'matplotlib.colors.to_rgb()' to convert string and\n",
      "      HTML color codes into the RGB tuple representation, eg.\n",
      "\n",
      "        from matplotlib.colors import ColorConverter\n",
      "        img = Draw.MolToImage(m, highlightAtoms=[1,2], highlightColor=ColorConverter().to_rgb('aqua'))\n",
      "        img.save(\"molecule.png\")\n",
      "\n",
      "RETURNS:\n",
      "\n",
      "  a PIL Image object\n",
      "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/gnn/lib/python3.8/site-packages/rdkit/Chem/Draw/__init__.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "Chem.Draw.MolToImage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=150x150>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles(\"C[C@H](O)c1ccccc1\")\n",
    "image = Chem.Draw.MolToImage(mol, size=(150,150), kekulize=True, highlightBonds=[0,1])\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=150x150>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 我使用了matplot里的一个工具，并指定高亮的颜色为'turquoise'，之后和MolToImage一并使用高亮了配对的部分构造。\n",
    "highlightColor=ColorConverter().to_rgb('turquoise')\n",
    "image = Chem.Draw.MolToImage(mol, size=(150,150), kekulize=True, highlightAtoms=(1,2,3,4), highlightBonds=[0,1],highlightColor=highlightColor)\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 保存\n",
    "image.save(\"./data/im.png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在新窗口中展示图片：`ShowMol()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mDraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mShowMol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mkekulize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mwedgeBonds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'RDKit Molecule'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mstayInFront\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Generates a picture of a molecule and displays it in a Tkinter window\n",
      "  \n",
      "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/gnn/lib/python3.8/site-packages/rdkit/Chem/Draw/__init__.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "Draw.ShowMol?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "TclError",
     "evalue": "no display name and no $DISPLAY environment variable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTclError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/DYZ/dyz1/learning/07-rdkit/03-rdkit可视化.ipynb Cell 8\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bmyubantu/DYZ/dyz1/learning/07-rdkit/03-rdkit%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m Draw\u001b[39m.\u001b[39;49mShowMol(\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmyubantu/DYZ/dyz1/learning/07-rdkit/03-rdkit%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m     mol,\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmyubantu/DYZ/dyz1/learning/07-rdkit/03-rdkit%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m     size\u001b[39m=\u001b[39;49m(\u001b[39m300\u001b[39;49m, \u001b[39m300\u001b[39;49m),\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmyubantu/DYZ/dyz1/learning/07-rdkit/03-rdkit%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m     kekulize\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,)\n",
      "File \u001b[0;32m~/anaconda3/envs/gnn/lib/python3.8/site-packages/rdkit/Chem/Draw/__init__.py:336\u001b[0m, in \u001b[0;36mShowMol\u001b[0;34m(mol, size, kekulize, wedgeBonds, title, stayInFront, **kwargs)\u001b[0m\n\u001b[1;32m    332\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mPIL\u001b[39;00m \u001b[39mimport\u001b[39;00m ImageTk\n\u001b[1;32m    334\u001b[0m img \u001b[39m=\u001b[39m MolToImage(mol, size, kekulize, wedgeBonds, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m--> 336\u001b[0m tkRoot \u001b[39m=\u001b[39m tkinter\u001b[39m.\u001b[39;49mTk()\n\u001b[1;32m    337\u001b[0m tkRoot\u001b[39m.\u001b[39mtitle(title)\n\u001b[1;32m    338\u001b[0m tkPI \u001b[39m=\u001b[39m ImageTk\u001b[39m.\u001b[39mPhotoImage(img)\n",
      "File \u001b[0;32m~/anaconda3/envs/gnn/lib/python3.8/tkinter/__init__.py:2261\u001b[0m, in \u001b[0;36mTk.__init__\u001b[0;34m(self, screenName, baseName, className, useTk, sync, use)\u001b[0m\n\u001b[1;32m   2259\u001b[0m         baseName \u001b[39m=\u001b[39m baseName \u001b[39m+\u001b[39m ext\n\u001b[1;32m   2260\u001b[0m interactive \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m-> 2261\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtk \u001b[39m=\u001b[39m _tkinter\u001b[39m.\u001b[39;49mcreate(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)\n\u001b[1;32m   2262\u001b[0m \u001b[39mif\u001b[39;00m useTk:\n\u001b[1;32m   2263\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_loadtk()\n",
      "\u001b[0;31mTclError\u001b[0m: no display name and no $DISPLAY environment variable"
     ]
    }
   ],
   "source": [
    "Draw.ShowMol(mol, size=(150,150), kekulize=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "保存图片`MolToFile(mol, filename, size, kekulize, wedgeBonds, …)`        \n",
    "参数基本同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Draw.MolToFile(mol, 'data/output.png', size=(150, 150))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 批量展示\n",
    "### 从DataFrame中展示\n",
    "从df中展示：`FrameToGridImage(frame, column, molsPerRow, subImgSize, legendsCol, …)`                   \n",
    "`frame`：DataFrame对象                  \n",
    "`column`：rdkit mol对象所在列                      \n",
    "`molsPerRow`,：每行显示的分子数                \n",
    "`subImgSize`：图片大小                \n",
    "`legendsCol`：标题所在列              \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x200>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('./data/batch.csv')\n",
    "PandasTools.AddMoleculeColumnToFrame(df,'SMILES','mol',includeFingerprints=True)\n",
    "PandasTools.FrameToGridImage(df, column='mol', molsPerRow=5, subImgSize=(200,200), legendsCol='Name')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从mol列表中展示             \n",
    "从列表生成分子结构：`MolsToGridImage(mols, maxMols, molsPerRow, subImgSize, legends, …)`       \n",
    "部分参数和上面的一致          \n",
    "`mols`：mol对象列表        \n",
    "`maxMols`：最多显示的分子数        \n",
    "`molsPerRow`,：每行显示的分子数         \n",
    "`subImgSize`：图片大小        \n",
    "`legends`：图题         \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mDraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMolsToGridImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmolsPerRow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msubImgSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mlegends\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mhighlightAtomLists\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mhighlightBondLists\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0museSVG\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mreturnPNG\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
      "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/gnn/lib/python3.8/site-packages/rdkit/Chem/Draw/__init__.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "Draw.MolsToGridImage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x600>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols = df.mol.tolist()\n",
    "legends = df.Name.tolist()\n",
    "legends = [str(l) for l in legends]\n",
    "Draw.MolsToGridImage(mols, molsPerRow=2, subImgSize=(300,300), legends=legends)  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D展示\n",
    "\n",
    "转换3D时，为了得到靠谱的三维构象，一般先加氢：`AddHs(mol)`             \n",
    "通过距离几何算法计算3D坐标：`EmbedMolecule(mol, randomSeed, …)`                 \n",
    "`mol`：mol对象       \n",
    "`randomSeed`：随机种子           \n",
    "转换完后再进行一步力场优化，比如MMFF94：`MMFFOptimizeMolecule(mol)`     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3d = Chem.MolFromSmiles('CNC(=O)N(N(CCCl)S(C)(=O)=O)S(C)(=O)=O')\n",
    "m3d = Chem.AddHs(m3d)\n",
    "AllChem.EmbedMolecule(m3d, randomSeed=3) # 利用EmbedMolecule模块将二维分子图转化为三维分子坐标\n",
    "AllChem.MMFFOptimizeMolecule(m3d) # 利用MMFFOptimizeMolecule模块对分子结构进行简单优化\n",
    "Draw.MolToImage(m3d, size=(250,250))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gnn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5 (default, Sep  4 2020, 07:30:14) \n[GCC 7.3.0]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "bbc91dbcb0051019ae97b751bde437ca7a1084c41c512133368dd180960dc485"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}