example_Fine-tuning_for_TRPM8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [13:48:20] Enabling RDKit 2019.09.3 jupyter extensions\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from rdkit import Chem, DataStructs\n",
    "from rdkit.Chem import AllChem, Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "from lstm_chem.utils.config import process_config\n",
    "from lstm_chem.model import LSTMChem\n",
    "from lstm_chem.finetuner import LSTMChemFinetuner\n",
    "from lstm_chem.data_loader import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model architecture from experiments/2020-03-24/LSTM_Chem/model_arch.json ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:00<00:00, 8680.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model checkpoint from experiments/2020-03-24/LSTM_Chem/checkpoints/LSTM_Chem-22-0.45.hdf5 ...\n",
      "Loaded the Model.\n",
      "loading SMILES...\n",
      "done.\n",
      "tokenizing SMILES...\n",
      "done.\n",
      "Train for 5 steps\n",
      "Epoch 1/12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 10s - loss: 1.0887WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 3s - loss: 1.0262 WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 1s - loss: 1.1727WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 1.0787WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 3s 613ms/step - loss: 1.0130\n",
      "Epoch 2/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.8828WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.9449WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.8082WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.7533WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 190ms/step - loss: 0.7937\n",
      "Epoch 3/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.6563WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.5775WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.6196WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.6419WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 217ms/step - loss: 0.7656\n",
      "Epoch 4/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 0s - loss: 1.1011WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 1.0081WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.8558WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.7208WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 192ms/step - loss: 0.7769\n",
      "Epoch 5/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.8545WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.6055WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.5212WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.6470WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 231ms/step - loss: 0.6699\n",
      "Epoch 6/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 1.1825WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 1.5216WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 1.1482WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 1.0270WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 176ms/step - loss: 0.9115\n",
      "Epoch 7/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.5708WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.5426WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.4831WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.4446WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 254ms/step - loss: 0.6257\n",
      "Epoch 8/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.3622WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.4441WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.3995WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.4970WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 232ms/step - loss: 0.5616\n",
      "Epoch 9/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.8128WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.6662WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.6218WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.5189WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 234ms/step - loss: 0.5275\n",
      "Epoch 10/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.3523WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.2913WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.2925WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/5 [=======================>......] - ETA: 0s - loss: 0.4202WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 236ms/step - loss: 0.4256\n",
      "Epoch 11/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.5183WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.4093WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.5753WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.5092WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 224ms/step - loss: 0.5061\n",
      "Epoch 12/12\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "1/5 [=====>........................] - ETA: 1s - loss: 0.2326WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "2/5 [===========>..................] - ETA: 0s - loss: 0.2691WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "3/5 [=================>............] - ETA: 0s - loss: 0.2737WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "4/5 [=======================>......] - ETA: 0s - loss: 0.2833WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n",
      "5/5 [==============================] - 1s 212ms/step - loss: 0.2708\n",
      "WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7a480c7cd0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = process_config('experiments/2020-03-24/LSTM_Chem/config.json')\n",
    "\n",
    "modeler = LSTMChem(config, session='finetune')\n",
    "finetune_dl = DataLoader(config, data_type='finetune')\n",
    "\n",
    "finetuner = LSTMChemFinetuner(modeler, finetune_dl)\n",
    "finetuner.finetune()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "finetuned_smiles = finetuner.sample(num=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./datasets/known_TRPM8-inhibitors_cleansed.smi') as f:\n",
    "    ksmiles = [l.rstrip() for l in f]\n",
    "kmols = [Chem.MolFromSmiles(smi) for smi in ksmiles]\n",
    "\n",
    "Kfps = []\n",
    "for mol in kmols:\n",
    "    bv = AllChem.GetMACCSKeysFingerprint(mol)\n",
    "    fp = np.zeros(len(bv))\n",
    "    DataStructs.ConvertToNumpyArray(bv, fp)\n",
    "    Kfps.append(fp)\n",
    "\n",
    "Klen = len(Kfps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./datasets/TRPM8_inhibitors_for_fine-tune.smi') as f:\n",
    "    fsmiles = [l.rstrip() for l in f]\n",
    "fmols = [Chem.MolFromSmiles(smi) for smi in fsmiles]\n",
    "\n",
    "Ffps, Fbvs = [], []\n",
    "for mol in fmols:\n",
    "    bv = AllChem.GetMACCSKeysFingerprint(mol)\n",
    "    Fbvs.append(bv)\n",
    "    \n",
    "    fp = np.zeros(len(bv))\n",
    "    DataStructs.ConvertToNumpyArray(bv, fp)\n",
    "    Ffps.append(fp)\n",
    "\n",
    "Flen = len(Ffps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sfps, Sbvs, smols = [], [], []\n",
    "for smi in finetuned_smiles:\n",
    "    mol = Chem.MolFromSmiles(smi)\n",
    "    smols.append(mol)\n",
    "    \n",
    "    bv = AllChem.GetMACCSKeysFingerprint(mol)\n",
    "    Sbvs.append(bv)\n",
    "    \n",
    "    fp = np.zeros(len(bv))\n",
    "    DataStructs.ConvertToNumpyArray(bv, fp)\n",
    "    Sfps.append(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Kfps + Ffps + Sfps\n",
    "pca = PCA(n_components=2, random_state=71)\n",
    "X = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.scatter(X[:Klen, 0], X[:Klen, 1],\n",
    "            c='w', edgecolors='k', label='Known TRPM8 inhibitors')\n",
    "plt.scatter(X[Klen:Klen + Flen, 0], X[Klen:Klen + Flen, 1],\n",
    "            s=200, c='r', marker='*', edgecolors='k', label='Selected training molecules')\n",
    "plt.scatter(X[Klen + Flen:, 0], X[Klen + Flen:, 1],\n",
    "            c='c', marker='+', label='TRPM8 fine tuned')\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs = []\n",
    "for Fbv in Fbvs:\n",
    "    idx = np.argmax(DataStructs.BulkTanimotoSimilarity(Fbv, Sbvs))\n",
    "    idxs.append(idx)\n",
    "nsmols = [smols[idx] for idx in idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "showmols = []\n",
    "for i, j in zip(fmols, nsmols):\n",
    "    showmols.append(i)\n",
    "    showmols.append(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x1000 at 0x7F7A4AFD64D0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolsToGridImage(showmols, molsPerRow=2)"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}