notebooks/TransferLearning.ipynb

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   "execution_count": 1,
   "id": "7fc51bea-3929-4d23-b769-1404dae34b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50d5ad4e-f31f-42fc-8bff-2936b68e457e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import os\n",
    "import json\n",
    "import csv\n",
    "import random\n",
    "import pandas as pd\n",
    "from utils.download import download_3d_similar_molecules\n",
    "from utils.chem import extract_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43ac46ea-9e95-4d95-814f-38280b20fda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths\n",
    "notebook = os.path.join(\".\")\n",
    "temp = os.path.join(notebook, \".temp\") # use to download temporary files (temporary downloads).\n",
    "if not os.path.exists(temp):\n",
    "    os.makedirs(temp)\n",
    "\n",
    "molecule_name = \"isoniazid\"\n",
    "temp = os.path.join(temp, molecule_name)\n",
    "\n",
    "if not os.path.exists(temp):\n",
    "    os.makedirs(temp)\n",
    "\n",
    "reinvent_prior_path = os.path.join(notebook, '..', 'models', 'reinvent.prior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52747510-b8b9-41ce-90a8-8a9ac3226194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download Smiles\n",
    "input_smiles = \"C1=CN=CC=C1C(=O)NN\"\n",
    "similar_str_smiles_path = os.path.join(temp, \"similar.json\")\n",
    "\n",
    "download_3d_similar_molecules(input_smiles, similar_str_smiles_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc492367-622a-42ca-b13e-902ca2db16e0",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "      <th>identifier</th>\n",
       "      <th>similarity</th>\n",
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       "  <tbody>\n",
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       "                                    smiles                      identifier  \\\n",
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       "4                          O=C(NO)c1ccncc1                ZINC15 : 4362829   \n",
       "..                                     ...                             ...   \n",
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       "396           CC(C)C[C@H](C)NC(=O)c1ccncc1       ZINC15 : ZINC000002069508   \n",
       "397         C[C@@H]([NH3+])CNC(=O)c1ccncc1               ZINC15 : 74532791   \n",
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       "399            CC(C)CC(C)NC(=O)C1=CC=NC=C1      ENAMINE-REAL : Z1499541656   \n",
       "\n",
       "     similarity  \n",
       "0      1.000000  \n",
       "1      0.769231  \n",
       "2      0.750000  \n",
       "3      0.653846  \n",
       "4      0.640000  \n",
       "..          ...  \n",
       "395    0.515152  \n",
       "396    0.515152  \n",
       "397    0.515152  \n",
       "398    0.515152  \n",
       "399    0.515152  \n",
       "\n",
       "[400 rows x 3 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the downloaded 3d similar structures.\n",
    "\n",
    "similar_str_smiles = {}\n",
    "with open(similar_str_smiles_path) as reader:\n",
    "    similar_str_smiles = json.load(reader)\n",
    "\n",
    "df = pd.DataFrame(data=similar_str_smiles)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4440a3ef-7da6-4360-a610-7082c795a764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training (80%), Validation(10%), Test(10%)\n",
    "df = df.sample(frac=1)\n",
    "\n",
    "# Define your split sizes\n",
    "train_size = int(0.8 * len(df))\n",
    "\n",
    "# Split your DataFrame\n",
    "train_df = df[:train_size]\n",
    "valid_df = df[train_size:]\n",
    "\n",
    "train_set_file = os.path.join(temp, 'training.smi')\n",
    "valid_set_file = os.path.join(temp, 'validation.smi')\n",
    "\n",
    "\n",
    "train_df.to_csv(train_set_file, sep=\"\\t\", index=False, header=False)\n",
    "valid_df.to_csv(valid_set_file, sep=\"\\t\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83a631f4-7ace-400c-8f6b-0bec6da81642",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transfer learning config. (Ref: https://github.com/MolecularAI/REINVENT4/blob/main/notebooks/Reinvent_TLRL.py)\n",
    "\n",
    "config_filename = os.path.join(temp, 'config.json')\n",
    "temp_models = os.path.join(temp, 'checkpoints')\n",
    "\n",
    "if not os.path.exists(temp_models):\n",
    "    os.mkdir(temp_models)\n",
    "\n",
    "new_model_path = os.path.join(temp_models, 'temp.model')\n",
    "\n",
    "reinvet_transfer_learning_parameter = {\n",
    "    \"run_type\": \"transfer_learning\",\n",
    "    \"device\": \"cpu\",\n",
    "    \"tb_logdir\": os.path.join(temp, 'tb_TL'),\n",
    "    \"parameters\": {\n",
    "        \"num_epochs\": 100,\n",
    "        \"save_every_n_epochs\": 2,\n",
    "        \"batch_size\": 50,\n",
    "        \"sample_batch_size\": 100,\n",
    "        \"input_model_file\": reinvent_prior_path,\n",
    "        \"output_model_file\": new_model_path,\n",
    "        \"smiles_file\": train_set_file,\n",
    "        \"validation_smiles_file\": valid_set_file,\n",
    "        \"standardize_smiles\": True,\n",
    "        \"randomize_smiles\": False,\n",
    "        \"randomize_all_smiles\": False,\n",
    "        \"internal_diversity\": True,\n",
    "    },\n",
    "}\n",
    "\n",
    "with open(config_filename, \"w\") as writer:\n",
    "    json.dump(reinvet_transfer_learning_parameter, writer, indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ddc710b-e44a-442c-ad52-1cee6c11514a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:31:06 <INFO> Started REINVENT 4.4.22 (C) AstraZeneca 2017, 2023 on 2024-07-28\n",
      "15:31:06 <INFO> Command line: /root/miniconda3/envs/reinvent-transfer-learning/bin/reinvent ./.temp/isoniazid/config.json -f json\n",
      "15:31:06 <INFO> User root on host Ank\n",
      "15:31:06 <INFO> Python version 3.11.9\n",
      "15:31:06 <INFO> PyTorch version 2.3.1+cu121, git d44533f9d073df13895333e70b66f81c513c1889\n",
      "15:31:06 <INFO> PyTorch compiled with CUDA version 12.1\n",
      "15:31:06 <INFO> RDKit version 2023.09.5\n",
      "15:31:06 <INFO> Platform Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35\n",
      "15:31:06 <INFO> Number of PyTorch CUDA devices 1\n",
      "15:31:06 <INFO> Using CPU x86_64\n",
      "15:31:06 <INFO> Writing TensorBoard summary to /mnt/d/projects/github/reinvent-transfer-learning/notebooks/.temp/isoniazid/tb_TL\n",
      "15:31:06 <INFO> Starting Transfer Learning\n",
      "15:31:06 <INFO> /mnt/d/projects/github/reinvent-transfer-learning/models/reinvent.prior has valid hash:\n",
      "{ 'comments': [],\n",
      "  'creation_date': 0,\n",
      "  'date_format': 'UNIX epoch',\n",
      "  'hash_id': '173568c36e1fc3d95cab289c7d31ce0b',\n",
      "  'hash_id_format': 'xxhash.xxh3_128_hex 3.4.1',\n",
      "  'model_id': '55d68f8a81c04f5a86304ebe1723a0ea',\n",
      "  'model_id_format': 'uuid.uuid4 3.10.12',\n",
      "  'origina_data_source': 'ChEMBL 25',\n",
      "  'updates': []}\n",
      "15:31:06 <INFO> Number of network parameters: 5,805,602\n",
      "15:31:06 <INFO> Network architecture:\n",
      "RNN(\n",
      "  (_embedding): Embedding(34, 256)\n",
      "  (_rnn): LSTM(256, 512, num_layers=3, batch_first=True)\n",
      "  (_linear): Linear(in_features=512, out_features=34, bias=True)\n",
      ")\n",
      "15:31:06 <INFO> Using generator Reinvent\n",
      "15:31:06 <INFO> Applying filter default to input SMILES\n",
      "15:31:06 <WARN> \"default\" filter: NNC(=O)C1=CC=C(I)C=N1 is invalid\n",
      "15:31:06 <WARN> \"default\" filter: NNC(=O)C1=CC=NC=C1I is invalid\n",
      "15:31:06 <WARN> \"default\" filter: NNC(=O)C1=CC=NC=C1I is invalid\n",
      "15:31:06 <WARN> \"default\" filter: NNC(=O)C1=CC=C(I)C=C1 is invalid\n",
      "15:31:06 <WARN> \"default\" filter: NNC(=O)C1=CC=C(I)C=C1 is invalid\n",
      "15:31:07 <INFO> Read 193 input SMILES from /mnt/d/projects/github/reinvent-transfer-learning/notebooks/.temp/isoniazid/training.smi\n",
      "15:31:07 <WARN> \"default\" filter: NNC(=O)C1=CC=NC=C1I is invalid\n",
      "15:31:07 <WARN> \"default\" filter: O=C(Nc1ccc(I)cc1)c1ccncc1 is invalid\n",
      "15:31:07 <WARN> \"default\" filter: NNC(=O)C1=CC=C(I)C=C1 is invalid\n",
      "15:31:07 <INFO> Read 71 validation SMILES from /mnt/d/projects/github/reinvent-transfer-learning/notebooks/.temp/isoniazid/validation.smi\n",
      "|\u001b[32m                                                                         \u001b[0m|00:00\u001b[0m\n",
      "Epoch 100: |\u001b[32m##############################################################\u001b[0m|03:04\u001b[0m\n",
      "0it [03:04, ?it/s]\n",
      "15:34:12 <INFO> Best validation loss (8.324) was at epoch 44\n",
      "15:34:12 <INFO> Peak main memory usage: 1138.535 MiB\n",
      "15:34:12 <INFO> Finished REINVENT on 2024-07-28\n"
     ]
    }
   ],
   "source": [
    "# Transfer Learning.\n",
    "\n",
    "!reinvent $config_filename -f json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05a8652c-19ea-478d-9f47-0c36d545000d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:34:16 <INFO> Started REINVENT 4.4.22 (C) AstraZeneca 2017, 2023 on 2024-07-28\n",
      "15:34:16 <INFO> Command line: /root/miniconda3/envs/reinvent-transfer-learning/bin/reinvent ./.temp/isoniazid/_config.json -f json\n",
      "15:34:16 <INFO> User root on host Ank\n",
      "15:34:16 <INFO> Python version 3.11.9\n",
      "15:34:16 <INFO> PyTorch version 2.3.1+cu121, git d44533f9d073df13895333e70b66f81c513c1889\n",
      "15:34:16 <INFO> PyTorch compiled with CUDA version 12.1\n",
      "15:34:16 <INFO> RDKit version 2023.09.5\n",
      "15:34:16 <INFO> Platform Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35\n",
      "15:34:16 <INFO> Number of PyTorch CUDA devices 1\n",
      "15:34:16 <INFO> Using CPU x86_64\n",
      "15:34:16 <INFO> Starting Sampling\n",
      "15:34:16 <INFO> /mnt/d/projects/github/reinvent-transfer-learning/notebooks/.temp/isoniazid/checkpoints/temp.model has valid hash:\n",
      "{ 'comments': ['TL'],\n",
      "  'creation_date': 0,\n",
      "  'date_format': 'UNIX epoch',\n",
      "  'hash_id': '070d833eb6f8bfd7d556a5c298994293',\n",
      "  'hash_id_format': 'xxhash.xxh3_128_hex 3.4.1',\n",
      "  'model_id': '55d68f8a81c04f5a86304ebe1723a0ea',\n",
      "  'model_id_format': 'uuid.uuid4 3.10.12',\n",
      "  'origina_data_source': 'ChEMBL 25',\n",
      "  'updates': [1722148452.1943145]}\n",
      "15:34:16 <INFO> Number of network parameters: 5,805,602\n",
      "15:34:16 <INFO> Network architecture:\n",
      "RNN(\n",
      "  (_embedding): Embedding(34, 256)\n",
      "  (_rnn): LSTM(256, 512, num_layers=3, batch_first=True)\n",
      "  (_linear): Linear(in_features=512, out_features=34, bias=True)\n",
      ")\n",
      "15:34:16 <INFO> Using generator Reinvent\n",
      "15:34:16 <INFO> Writing sampled SMILES to CSV file ./.temp/isoniazid/output.csv\n",
      "15:34:16 <INFO> Sampling 15000 SMILES from model ./.temp/isoniazid/checkpoints/temp.model\n",
      "15:35:19 <INFO> Peak main memory usage: 1412.938 MiB\n",
      "15:35:19 <INFO> Finished REINVENT on 2024-07-28\n"
     ]
    }
   ],
   "source": [
    "# Running new model\n",
    "new_model_config_path = os.path.join(temp, '_config.json')\n",
    "output_smiles = os.path.join(temp, 'output.csv')\n",
    "config = {\n",
    "    \"run_type\": \"sampling\",\n",
    "    \"device\": \"cpu\",\n",
    "    \"parameters\": {\n",
    "        \"model_file\": new_model_path,\n",
    "        \"output_file\": output_smiles,\n",
    "        \"num_smiles\": 15_000,\n",
    "        \"unique_molecules\": True,\n",
    "        \"randomize_smiles\": True,\n",
    "    }\n",
    "}\n",
    "\n",
    "with open(new_model_config_path, \"w\") as writer:\n",
    "    json.dump(config, writer, indent=2)\n",
    "\n",
    "!reinvent $new_model_config_path -f json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fdf5082-b225-4c12-8493-d86a909dc64b",
   "metadata": {},
   "source": [
    "# Traning a new classifier model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04e43224-17c8-4542-8af1-b016fbabaf55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.79 s, sys: 152 ms, total: 1.94 s\n",
      "Wall time: 3.48 s\n"
     ]
    },
    {
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       "      <th>4995</th>\n",
       "      <td>CCN1C(=O)/C(=C/C=C2\\SC3=CC(C(F)(F)F)=CC=C3N2C)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>COC(=O)C1=C(F)C=CC=C1C1=CC=C(CNC(=O)[C@@](C)(O...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>CCOC(=O)C1=C(NC(=O)CSC2=NN=C(CC3=CC=CC=C3)O2)S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>C/C=C(/C)C(=O)OC[C@H]1C2=C(C[C@H]3[C@H]4C5=C(C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>O=C(NC1=CC(C2=CC=C3C=CN=CC3=C2)=CC=N1)C1CC1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0\n",
       "0     O=C1NC(=O)C(CC2=CC=C(N3CCC(NC[C@H](O)C4=CC=CC(...\n",
       "1                COC(=O)/C(=C/C1=CC=C(C)C=C1)CP(=O)(O)O\n",
       "2                  CCOC1=CC=C(CCNC(=O)C2=CC=CS2)C=C1OCC\n",
       "3     CCCCOC1=CC=C(C#CC2=CC=C(S(=O)(=O)N[C@H](CC3=CN...\n",
       "4     CCCCN1C(N)=C(N(CC(C)C)C(=O)C2=CC(C3=CC=C(OC)C=...\n",
       "...                                                 ...\n",
       "4995  CCN1C(=O)/C(=C/C=C2\\SC3=CC(C(F)(F)F)=CC=C3N2C)...\n",
       "4996  COC(=O)C1=C(F)C=CC=C1C1=CC=C(CNC(=O)[C@@](C)(O...\n",
       "4997  CCOC(=O)C1=C(NC(=O)CSC2=NN=C(CC3=CC=CC=C3)O2)S...\n",
       "4998  C/C=C(/C)C(=O)OC[C@H]1C2=C(C[C@H]3[C@H]4C5=C(C...\n",
       "4999        O=C(NC1=CC(C2=CC=C3C=CN=CC3=C2)=CC=N1)C1CC1\n",
       "\n",
       "[5000 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "negative_entries = []\n",
    "# Make sure to download and keep the reference library in .temp folder.\n",
    "# Download it from here: https://github.com/ersilia-os/groverfeat/blob/main/data/reference_library.csv\n",
    "reference_smiles_path = os.path.join(notebook, '.temp', 'reference_library.csv')\n",
    "\n",
    "with open(reference_smiles_path) as reader:\n",
    "    rows = csv.reader(reader)\n",
    "    negative_entries = random.sample(list(rows), 5000)\n",
    "\n",
    "negative_output = os.path.join(temp, 'negative.csv')\n",
    "df = pd.DataFrame(data=negative_entries)\n",
    "df.to_csv(negative_output, index=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b3e4fabb-3b1f-4e85-af61-84df5010df84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data for classifier model\n",
    "negatives = []\n",
    "positives = []\n",
    "\n",
    "# Positives are all the smiles downloaded using cheese api\n",
    "with open(similar_str_smiles_path) as reader:\n",
    "    data = json.load(reader)\n",
    "    \n",
    "    for entry in data:\n",
    "        positives.append(entry['smiles'])\n",
    "\n",
    "# Negatives are all the smiles generated using reinvent\n",
    "with open(negative_output) as reader:\n",
    "    data = csv.reader(reader)\n",
    "    next(data, None) # skipping header\n",
    "    for row in data:\n",
    "        negatives.append(row[0])\n",
    "\n",
    "labels = [1] * len(positives)\n",
    "labels += [0] * len(negatives)\n",
    "\n",
    "total = positives + negatives\n",
    "features = extract_features(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93996458-3258-450b-a914-cab9cc24052e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[flaml.automl.logger: 07-28 15:35:35] {1680} INFO - task = classification\n",
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      "[flaml.automl.logger: 07-28 15:35:35] {1901} INFO - List of ML learners in AutoML Run: ['rf']\n",
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      "[flaml.automl.logger: 07-28 15:37:33] {2628} INFO - retrain rf for 0.2s\n",
      "[flaml.automl.logger: 07-28 15:37:33] {2631} INFO - retrained model: RandomForestClassifier(criterion='entropy', max_features=0.037726749209647005,\n",
      "                       max_leaf_nodes=23, n_estimators=7, n_jobs=-1,\n",
      "                       random_state=12032022)\n",
      "[flaml.automl.logger: 07-28 15:37:33] {1931} INFO - fit succeeded\n",
      "[flaml.automl.logger: 07-28 15:37:33] {1932} INFO - Time taken to find the best model: 4.713239669799805\n",
      "Accuracy: 0.9962962962962963\n"
     ]
    }
   ],
   "source": [
    "# Training\n",
    "from flaml import AutoML\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X = pd.DataFrame(features)\n",
    "y = pd.Series(labels)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "automl = AutoML()\n",
    "automl_settings = {\n",
    "    \"time_budget\": 120,  # time budget in seconds\n",
    "    \"metric\": 'f1',  # metric to optimize\n",
    "    \"task\": 'classification',\n",
    "    \"estimator_list\": ['rf'] # random forest\n",
    "}\n",
    "\n",
    "automl.fit(X_train, y_train, **automl_settings)\n",
    "\n",
    "# Checking accuracy\n",
    "y_pred = automl.predict(X_test)\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35b413c5-fd6b-48c8-a0c9-c9215c34fe99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing generated output (the output generated by the new model.)\n",
    "\n",
    "gen_out = []\n",
    "\n",
    "with open(output_smiles) as reader:\n",
    "    data = csv.reader(reader)\n",
    "    next(data, None) # skipping header\n",
    "    for row in data:\n",
    "        gen_out.append(row[0])\n",
    "\n",
    "gen_out_feature = extract_features(gen_out)\n",
    "gen_out_feature = pd.DataFrame(gen_out_feature)\n",
    "\n",
    "prediction = automl.predict_proba(gen_out_feature) # predict_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5418117-4c62-4fdf-902b-967c5bd6d7dd",
   "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>smile</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>CSCONC(=O)c1ccncc1</td>\n",
       "      <td>0.988229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>CNC(=N)NC(=O)c1ccncc1</td>\n",
       "      <td>0.988229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>CCCCCCCCCCNC(=O)c1ccncc1</td>\n",
       "      <td>0.988229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>C#CC(C)(C)NC(=O)c1ccncc1</td>\n",
       "      <td>0.988229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>CCCNCC(C)(C)NC(=O)c1ccncc1</td>\n",
       "      <td>0.988229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>COC(=N)Cc1ccncc1</td>\n",
       "      <td>0.205787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>CCC1(CC)C(O)c2ccccc2C1c1cccnc1</td>\n",
       "      <td>0.204998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>CC(CO)CNC(=O)c1cnccn1</td>\n",
       "      <td>0.204620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>CCCCCCC(C)=NNC(=O)c1cccnc1</td>\n",
       "      <td>0.204620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>CC(C)ONC(=O)c1cnccn1</td>\n",
       "      <td>0.204620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               smile     score\n",
       "668               CSCONC(=O)c1ccncc1  0.988229\n",
       "482            CNC(=N)NC(=O)c1ccncc1  0.988229\n",
       "120         CCCCCCCCCCNC(=O)c1ccncc1  0.988229\n",
       "1025        C#CC(C)(C)NC(=O)c1ccncc1  0.988229\n",
       "413       CCCNCC(C)(C)NC(=O)c1ccncc1  0.988229\n",
       "...                              ...       ...\n",
       "1052                COC(=N)Cc1ccncc1  0.205787\n",
       "377   CCC1(CC)C(O)c2ccccc2C1c1cccnc1  0.204998\n",
       "1017           CC(CO)CNC(=O)c1cnccn1  0.204620\n",
       "386       CCCCCCC(C)=NNC(=O)c1cccnc1  0.204620\n",
       "205             CC(C)ONC(=O)c1cnccn1  0.204620\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_prediction_output = os.path.join(temp, 'ml_output.csv')\n",
    "\n",
    "output = []\n",
    "for idx, mol in enumerate(gen_out):\n",
    "    output.append({\"smile\": gen_out[idx], \"score\": prediction[idx][1]})\n",
    "\n",
    "with_ml_df = pd.DataFrame(data=output)\n",
    "with_ml_df = with_ml_df.sort_values(['score'], ascending=False)\n",
    "with_ml_df = with_ml_df[:1000] # Picking top 1,000\n",
    "with_ml_df.to_csv(ml_prediction_output, index=False, header=False)\n",
    "with_ml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "876bfdf2-10fe-4e4d-96c5-d2f9ba21c64e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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