003b_smiles_basic_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "complete-hybrid",
   "metadata": {},
   "source": [
    "### Obtain basic molecular properties for enumerated acid-amine coupled products\n",
    "### This notebook does not do any filtering, and plots all 222,740 structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "typical-colors",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, Draw\n",
    "from rdkit.Chem import rdMolDescriptors, Descriptors\n",
    "\n",
    "from rdkit import RDLogger\n",
    "lg = RDLogger.logger()\n",
    "lg.setLevel(RDLogger.CRITICAL)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5484b003",
   "metadata": {},
   "outputs": [],
   "source": [
    "import molprops_utils\n",
    "import multiprocessing as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0807079",
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_data_df(df):\n",
    "    \"\"\"takes a dataframe of values and does interpolation\"\"\"\n",
    "    \n",
    "    prop_dict = {}\n",
    "    minmax_dict = {}\n",
    "    \n",
    "    for p in list(df):\n",
    "        minv = np.min(df[p])\n",
    "        maxv = np.max(df[p])\n",
    "        minmax_dict[p] = (minv,maxv)\n",
    "        prop_dict[p] = np.interp(df[p],(minv,maxv),(0,1))\n",
    "        \n",
    "    df_out = pd.DataFrame(data=prop_dict)    \n",
    "    return df_out, minmax_dict\n",
    "\n",
    "\n",
    "# a new helper function\n",
    "def interp_point(data_point,minmax_dict):\n",
    "    dict_out = {}\n",
    "    for i,k in enumerate(minmax_dict.keys()):\n",
    "        minv,maxv = minmax_dict[k]\n",
    "        dict_out[k] = np.interp(data_point[i],(minv,maxv),(0,1))\n",
    "        \n",
    "    return dict_out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92acb518",
   "metadata": {},
   "source": [
    "### load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71ad501c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./data_files/smiles_min_dist_dbank_props.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f8fc92",
   "metadata": {},
   "source": [
    "### Examine distributions of ring count and bond edit distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6334c4ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogP -3.2043000000000004 0.5325 2.1965000000000003\n",
      "MW 48.044 101.14899999999999 119.16400000000004\n",
      "HBD 0.0 1.0 3.0\n",
      "HBA 0.0 1.0 3.0\n",
      "PSA 0.0 29.1 66.48\n",
      "ROTB 0.0 2.0 5.0\n",
      "FSP3 0.0 0.8 1.0\n",
      "SSSR 0.0 0.0 13.0\n",
      "QED 0.0996481770580875 0.5398542153220981 0.6460803466915024\n"
     ]
    }
   ],
   "source": [
    "amide_props = np.array(molprops_utils.getallprops(\"CCC(NCC)=O\"))\n",
    "# get the x axis text.\n",
    "for ip, prop in enumerate([\"LogP\",\"MW\",\"HBD\",\"HBA\",\"PSA\",\"ROTB\",\"FSP3\",\"SSSR\",\"QED\"]):\n",
    "    print(prop,np.min(data[prop]),amide_props[ip],np.max(data[prop]))\n",
    "    \n",
    "# Rounded versions of this cell's output, for plotting purposes.\n",
    "\n",
    "x_locs_text = {'LogP': [-3.20, 0.53, 2.20],\n",
    " 'MW': [48, 101, 119],\n",
    " 'HBD': [0, 1, 3],\n",
    " 'HBA': [0, 1, 3],\n",
    " 'PSA': [0, 29, 66],\n",
    " 'ROTB': [0, 2, 5],\n",
    " 'FSP3': [0.0, 0.8, 1.0],\n",
    " 'SSSR': [0, 0, 13],\n",
    " 'QED': [0.10, 0.54, 0.64],\n",
    " 'Rings': [0, 0, 4]}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd9804a",
   "metadata": {},
   "source": [
    "### interpolate for plotting\n",
    "the interpolation is for better control of axes. Can also be done without interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b385a2a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "props_interp, minmax_dict =  interpolate_data_df(data[[\"LogP\",\"MW\",\"HBD\",\"HBA\",\"PSA\",\"ROTB\",\"FSP3\",\"SSSR\",\"QED\"]])\n",
    "\n",
    "# interpolated difference for plotting location\n",
    "amide_interp = interp_point(amide_props,minmax_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7246cce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this order is different, due to the sorting in Figure 3.\n",
    "\n",
    "props_interp = props_interp[['HBD', 'PSA', 'FSP3', 'MW', 'HBA', 'QED', 'LogP', 'ROTB', 'SSSR']].copy()\n",
    "df_plot = props_interp.melt(var_name = \"property\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a7ec43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_locs_vals = {}\n",
    "for k in amide_interp.keys():\n",
    "    i = minmax_dict[k]\n",
    "    minmax_interp = np.interp(i,(min(i),max(i)),(0,1))\n",
    "    x_locs_vals[k] = [minmax_interp[0],amide_interp[k],minmax_interp[1]]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f55c9106",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ik, k in enumerate(minmax_dict.keys()):\n",
    "    i = minmax_dict[k]\n",
    "#     print(k,i[0],amide_props[ik],i[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61a544b",
   "metadata": {},
   "source": [
    "### plot kernel density figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17f46a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 875.985x875.985 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pal = sns.cubehelix_palette(10, rot=-.25, light=.7)\n",
    "sns.set(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)},font_scale=1.2)\n",
    "g = sns.FacetGrid(df_plot,col = \"property\",\n",
    "                  aspect=1,height=1.16798*2.5,col_wrap=3,\n",
    "                  sharex=False,sharey=False,palette=\"plasma\")\n",
    "\n",
    "g.map(sns.kdeplot, \"value\",clip_on=True,fill=True,bw_method=0.2);\n",
    "g.set(yticks=[])\n",
    "\n",
    "g.despine(top=False, right=False)\n",
    "titles = ['HBD', 'PSA', 'FSP3', 'MW', 'HBA', 'QED', 'LogP', 'ROTB', 'SSSR']\n",
    "         \n",
    "\n",
    "for i, ax in enumerate(g.axes.flat): # set every-other axis for testing purposes\n",
    "    title = titles[i]\n",
    "    \n",
    "    \n",
    "    M_loc = amide_interp[title]\n",
    "    ax.set_ylabel(\"\")\n",
    "    ax.set_xticks([])\n",
    "    \n",
    "    # was removed for publishing purposes\n",
    "    ax.set_xticks(x_locs_vals[title])\n",
    "    ax.set_xticklabels(x_locs_text[title],rotation=0,fontsize=18)\n",
    "    \n",
    "    ax.set_xlim(-.5,1.5)\n",
    "    greys = 0.65\n",
    "    ax.axvline(x_locs_vals[title][1], color=(greys,greys,greys), linewidth=2)\n",
    "    if title == \"SSSR\":\n",
    "        ax.set_title(\"Rings\", fontweight='bold',fontsize=15)\n",
    "    else:\n",
    "        ax.set_title(title, fontweight='bold',fontsize=15)\n",
    "\n",
    "g.set_xlabels(\"\")\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.41)\n",
    "\n",
    "# g.fig.suptitle(r\"Molecular Properties\",fontweight='bold',fontsize=16)\n",
    "plt.savefig(f\"./figures/SI_KDEs_3.svg\",format=\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8708c5e4",
   "metadata": {},
   "source": [
    "### calculate PMI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff9a0b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# around 20 minutes on 32 cores \n",
    "if __name__ == '__main__':\n",
    "    with mp.Pool(32) as p:\n",
    "        result = p.map(molprops_utils.cal_pmi, data.smiles,chunksize=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6a9b18d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"pmi_x\"] = [i[0] for i in result]\n",
    "data[\"pmi_y\"] = [i[1] for i in result]\n",
    "# data.drop(\"pmi\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0aad6231",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[[\"smiles\",\"pmi_x\",\"pmi_y\"]].to_csv(\"./data_files/smiles_pmi.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b187ccd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pmi = pd.read_csv(\"./data_files/smiles_pmi.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e931a880",
   "metadata": {},
   "source": [
    "### collect data separately for the 4 amides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc6e53f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "      <th>pmi_x</th>\n",
       "      <th>pmi_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CCNC(=O)CC</td>\n",
       "      <td>0.130135</td>\n",
       "      <td>0.897591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>C=CC(=O)NCC</td>\n",
       "      <td>0.186604</td>\n",
       "      <td>0.888132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>C=CNC(=O)CC</td>\n",
       "      <td>0.164827</td>\n",
       "      <td>0.851351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2669</th>\n",
       "      <td>C=CNC(=O)C=C</td>\n",
       "      <td>0.234627</td>\n",
       "      <td>0.765373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            smiles     pmi_x     pmi_y\n",
       "82      CCNC(=O)CC  0.130135  0.897591\n",
       "772    C=CC(=O)NCC  0.186604  0.888132\n",
       "858    C=CNC(=O)CC  0.164827  0.851351\n",
       "2669  C=CNC(=O)C=C  0.234627  0.765373"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amide_smiles = [Chem.MolToSmiles(Chem.MolFromSmiles(s)) for s in \"CCC(NCC)=O.C=CC(NCC)=O.C=CC(NC=C)=O.CCC(NC=C)=O\".split(\".\")]\n",
    "\n",
    "amide_data = data_pmi[data_pmi.smiles.isin(amide_smiles)]\n",
    "amide_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c29eaed",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pmi.dropna(subset = ['pmi_x'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01120cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pmi.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56c0b3b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "amide_smiles = [Chem.MolToSmiles(Chem.MolFromSmiles(s)) for s in \"CCC(NCC)=O.C=CC(NCC)=O.C=CC(NC=C)=O.CCC(NC=C)=O\".split(\".\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2d1f3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pmi_dict = {}\n",
    "\n",
    "for s in amide_smiles:\n",
    "    \n",
    "    s_pmi = []\n",
    "    for _ in range(100):\n",
    "        s_pmi.append(molprops_utils.cal_pmi(s))\n",
    "    pmi_dict[s] = s_pmi\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2cc6f0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 10800x10800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(18,18),dpi=600)\n",
    "fig, ax = plt.subplots(figsize=(18,18),dpi=600)\n",
    "\n",
    "h = ax.hist2d(list(data_pmi.pmi_x), list(data_pmi.pmi_y),bins=75,cmin=1,cmap=\"plasma\")\n",
    "\n",
    "\n",
    "ax.set_xlim((0,1))\n",
    "ax.set_ylim((0.5,1))\n",
    "ax.plot([0,.5], [1,0.5],color='gray')\n",
    "ax.plot([.5,1], [0.5,1],color='gray')\n",
    "ax.plot([0,1], [1,1],color='gray')\n",
    "x0,x1 = ax.get_xlim()\n",
    "y0,y1 = ax.get_ylim()\n",
    "ax.set_aspect(abs(x1-x0)/abs(y1-y0))\n",
    "divider = make_axes_locatable(ax)\n",
    "plt.colorbar(h[3],shrink=0.4)\n",
    "\n",
    "# the 4 amides \n",
    "for s in pmi_dict:\n",
    "    pmi_data = np.array(pmi_dict[s])\n",
    "    \n",
    "    ax.scatter(np.mean(pmi_data[:,0]),np.mean(pmi_data[:,1]),alpha=1,s=40,color=\"k\")\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "fig.savefig('./figures/SI_pmi_200k.svg', format=\"svg\")\n",
    "# plt.close(fig)\n",
    "pass"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}