cigin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "import sys\n",
    "import warnings\n",
    "from collections import OrderedDict\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from rdkit import Chem, DataStructs\n",
    "from rdkit.Chem import rdMolDescriptors as rdDesc\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import rdBase\n",
    "from rdkit import RDLogger\n",
    "\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils import data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.insert(0, './scripts/')\n",
    "from molecular_graph import ConstructMolecularGraph\n",
    "from models import Cigin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cigin(\n",
       "  (solute_pass): MessagePassing(\n",
       "    (U_0): Linear(in_features=90, out_features=40, bias=True)\n",
       "    (U_1): Linear(in_features=90, out_features=40, bias=True)\n",
       "    (U_2): Linear(in_features=90, out_features=40, bias=True)\n",
       "    (M_0): Linear(in_features=80, out_features=40, bias=True)\n",
       "    (M_1): Linear(in_features=80, out_features=40, bias=True)\n",
       "    (M_2): Linear(in_features=80, out_features=40, bias=True)\n",
       "  )\n",
       "  (solvent_pass): MessagePassing(\n",
       "    (U_0): Linear(in_features=90, out_features=40, bias=True)\n",
       "    (U_1): Linear(in_features=90, out_features=40, bias=True)\n",
       "    (U_2): Linear(in_features=90, out_features=40, bias=True)\n",
       "    (M_0): Linear(in_features=80, out_features=40, bias=True)\n",
       "    (M_1): Linear(in_features=80, out_features=40, bias=True)\n",
       "    (M_2): Linear(in_features=80, out_features=40, bias=True)\n",
       "  )\n",
       "  (lstm_solute): LSTM(80, 40)\n",
       "  (lstm_solvent): LSTM(80, 40)\n",
       "  (lstm_gather_solute): LSTM(320, 160)\n",
       "  (lstm_gather_solvent): LSTM(320, 160)\n",
       "  (first_layer): Linear(in_features=640, out_features=360, bias=True)\n",
       "  (second_layer): Linear(in_features=360, out_features=200, bias=True)\n",
       "  (third_layer): Linear(in_features=200, out_features=120, bias=True)\n",
       "  (fourth_layer): Linear(in_features=120, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=Cigin().to(device)\n",
    "model.load_state_dict(torch.load('weights/cigin.tar'))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting free energy of solvation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted free energy of solvation:  -7.81099796295166\n"
     ]
    }
   ],
   "source": [
    "solute = 'OCCN'\n",
    "solvent = 'OCC'\n",
    "delta_g, interaction_map =  model(solute,solvent)\n",
    "print(\"Predicted free energy of solvation: \",str(delta_g.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of interaction map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying min max normalization across the interaction map\n",
    "\n",
    "min_value = min(interaction_map.flatten())\n",
    "max_value = max(interaction_map.flatten())\n",
    "interaction_map = ((interaction_map-min_value)/(max_value-min_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x14e100159030>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolFromSmiles(solute) #OCCN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solvent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x14e1001590d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolFromSmiles(solvent) #OCC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interaction map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 15.0, 'Solvent')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(interaction_map,annot=True,xticklabels=['O','C','C'],yticklabels=['O','C','C','N'],cmap='viridis')\n",
    "plt.ylabel('Solute',size='15')\n",
    "plt.xlabel('Solvent',size='15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}