{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wWSzEAjavzrb", "outputId": "3c8987e4-5537-46ed-fc4b-0988e03b376c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: bunch in /usr/local/lib/python3.8/dist-packages (1.0.1)\n" ] } ], "source": [ "\n", "pip install bunch" ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PXp_V--04b4N", "outputId": "35b31fa0-1cde-4ddc-b82d-af62805a5c76" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "7mgq8uVbyW4d" }, "source": [ "## Utility Codes" ] }, { "cell_type": "markdown", "metadata": { "id": "oyGzbJOiyau5" }, "source": [ "### Parameter Setting" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FSYvCcgkvxtK" }, "outputs": [], "source": [ "import os\n", "import time\n", "import json\n", "from bunch import Bunch\n", "\n", "import pandas as pd\n", "\n", "df= pd.read_csv('vocab.csv')\n", "vocab = df['Smiles'].tolist()\n", "\n", "def get_config_from_json(json_file):\n", " with open(json_file, 'r') as config_file:\n", " config_dict = json.load(config_file)\n", " config = Bunch(config_dict)\n", " return config\n", "\n", "\n", "def process_config(json_file):\n", " config = get_config_from_json(json_file)\n", " config.config_file = json_file\n", " config.exp_dir = os.path.join(\n", " 'experiments', time.strftime('%Y-%m-%d/', time.localtime()),\n", " config.exp_name)\n", " config.tensorboard_log_dir = os.path.join(\n", " 'experiments', time.strftime('%Y-%m-%d/', time.localtime()),\n", " config.exp_name, 'logs/')\n", " config.checkpoint_dir = os.path.join(\n", " 'experiments', time.strftime('%Y-%m-%d/', time.localtime()),\n", " config.exp_name, 'checkpoints/')\n", " return config" ] }, { "cell_type": "markdown", "metadata": { "id": "tJp7vxoyyhej" }, "source": [ "### Creating Directory" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Bx9GeQ9qwEXy" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "\n", "def create_dirs(dirs):\n", " try:\n", " for dir_ in dirs:\n", " if not os.path.exists(dir_):\n", " os.makedirs(dir_)\n", " except Exception as err:\n", " print(f'Creating directories error: {err}')\n", " sys.exit()" ] }, { "cell_type": "markdown", "metadata": { "id": "7ME9uPedQj00" }, "source": [ "## Building Function Vocabulary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "5MokMxOEQiaO", "outputId": "d4eb662d-400e-41f5-ed34-f1278ab6520b" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Unnamed: 0 Len Functional grp name \\\n", "0 0 7 Acid anhydrides \n", "1 1 8 Diacyl peroxides \n", "2 2 15 Phenyl esters \n", "3 3 5 Isocyanates \n", "4 4 8 β-Lactams \n", "... ... ... ... \n", "1240 1240 12 SA46 \n", "1241 1241 11 SA47 \n", "1242 1242 19 SA48 \n", "1243 1243 15 SA49 \n", "1244 1244 6 Primary amines (strict) \n", "\n", " SMARTS Smiles \\\n", "0 [CX3](=[OX1])[OX2][CX3](=[OX1]) O=COC=O \n", "1 [CX3](=[OX1])[OX2][OX2][CX3](=[OX1]) O=COOC=O \n", "2 [cR1]1([OX2][CX3](=[OX1])[#6])[cR1][cR1][cR1][... CC(=O)Oc1ccccc1 \n", "3 [NX2]=[CX2]=[OX1] N=C=O \n", "4 [NX3]1[CX4][CX4][CX3]1(=[OX1]) O=C1CCN1 \n", "... ... ... \n", "1240 [#6]-1-2-[#6]=[#6]-[#6](-[#6]-1)-[#6]-[#6]-2 C1=CC2CCC1C2 \n", "1241 [#6]-1-2-[#6]-[#6]-[#6](-[#6]-1)-[#6]-[#6]-2 C1CC2CCC1C2 \n", "1242 c1(c(cccc1)-[#8]-c2ccccc2)-[Br] Brc1ccccc1Oc1ccccc1 \n", "1243 c1-2c(cccc1)-[#6]-[#6]-[#6]-2 c1ccc2c(c1)CCC2 \n", "1244 [NX3H2][CX4&!$([CX4]([NH2])[O,N,S,P])] C[NH2] \n", "\n", " Validity \n", "0 Valid \n", "1 Valid \n", "2 Valid \n", "3 Valid \n", "4 Valid \n", "... ... \n", "1240 Valid \n", "1241 Valid \n", "1242 Valid \n", "1243 Valid \n", "1244 Valid \n", "\n", "[1245 rows x 6 columns]" ], "text/html": [ "\n", "
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Unnamed: 0LenFunctional grp nameSMARTSSmilesValidity
007Acid anhydrides[CX3](=[OX1])[OX2][CX3](=[OX1])O=COC=OValid
118Diacyl peroxides[CX3](=[OX1])[OX2][OX2][CX3](=[OX1])O=COOC=OValid
2215Phenyl esters[cR1]1([OX2][CX3](=[OX1])[#6])[cR1][cR1][cR1][...CC(=O)Oc1ccccc1Valid
335Isocyanates[NX2]=[CX2]=[OX1]N=C=OValid
448β-Lactams[NX3]1[CX4][CX4][CX3]1(=[OX1])O=C1CCN1Valid
.....................
1240124012SA46[#6]-1-2-[#6]=[#6]-[#6](-[#6]-1)-[#6]-[#6]-2C1=CC2CCC1C2Valid
1241124111SA47[#6]-1-2-[#6]-[#6]-[#6](-[#6]-1)-[#6]-[#6]-2C1CC2CCC1C2Valid
1242124219SA48c1(c(cccc1)-[#8]-c2ccccc2)-[Br]Brc1ccccc1Oc1ccccc1Valid
1243124315SA49c1-2c(cccc1)-[#6]-[#6]-[#6]-2c1ccc2c(c1)CCC2Valid
124412446Primary amines (strict)[NX3H2][CX4&!$([CX4]([NH2])[O,N,S,P])]C[NH2]Valid
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\n", " " ] }, "metadata": {}, "execution_count": 36 } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('functions.csv')\n", "df\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VpdiNPeLQiNb", "outputId": "a6cd7530-4f57-4c17-a870-cc846e5d0c16" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(673, 6)\n" ] } ], "source": [ "df_filtered = df[df.Len < 13]\n", "print(df_filtered.shape)\n", "vocab = df_filtered['Smiles'].tolist()" ] }, { "cell_type": "markdown", "metadata": { "id": "82goiSvmynck" }, "source": [ "## SMILES TOKENIZER" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CCJOzUKwuydA" }, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "class SmilesTokenizer(object):\n", " def __init__(self):\n", " atoms = [\n", " 'Al', 'As', 'B', 'Br', 'C', 'Cl', 'F', 'H', 'I', 'K', 'Li', 'N',\n", " 'Na', 'O', 'P', 'S', 'Se', 'Si', 'Te'\n", " ]\n", " special = [\n", " '(', ')', '[', ']', '=', '#', '%', '0', '1', '2', '3', '4', '5',\n", " '6', '7', '8', '9', '+', '-', 'se', 'te', 'c', 'n', 'o', 's'\n", " ]\n", "\n", " function = vocab\n", "\n", "\n", "\n", " padding = ['G', 'A', 'E']\n", "\n", " self.table = sorted(function, key=len, reverse=True) + sorted(vocab, key=len, reverse=True) + padding + special\n", " table_len = len(self.table)\n", "\n", " self.table_12_chars = list(filter(lambda x: len(x) == 12, self.table))\n", " self.table_11_chars = list(filter(lambda x: len(x) == 11, self.table))\n", " self.table_10_chars = list(filter(lambda x: len(x) == 10, self.table))\n", " self.table_9_chars = list(filter(lambda x: len(x) == 9, self.table))\n", " self.table_8_chars = list(filter(lambda x: len(x) == 8, self.table))\n", " self.table_7_chars = list(filter(lambda x: len(x) == 7, self.table))\n", " self.table_6_chars = list(filter(lambda x: len(x) == 6, self.table))\n", " self.table_5_chars = list(filter(lambda x: len(x) == 5, self.table))\n", " self.table_4_chars = list(filter(lambda x: len(x) == 4, self.table))\n", " self.table_3_chars = list(filter(lambda x: len(x) == 3, self.table))\n", " self.table_2_chars = list(filter(lambda x: len(x) == 2, self.table))\n", " self.table_1_chars = list(filter(lambda x: len(x) == 1, self.table))\n", "\n", "\n", "\n", " self.one_hot_dict = {}\n", " for i, symbol in enumerate(self.table):\n", " vec = np.zeros(table_len, dtype=np.float32)\n", " vec[i] = 1\n", " self.one_hot_dict[symbol] = vec\n", "\n", " def tokenize(self, smiles):\n", "\n", " N = len(smiles)\n", " token = []\n", " i = 0\n", " while (i < N):\n", " c1 = smiles[i]\n", " c2 = smiles[i:i + 2]\n", " c3 = smiles[i:i + 3]\n", " c4 = smiles[i:i + 4]\n", " c5 = smiles[i:i + 5]\n", " c6 = smiles[i:i + 6]\n", " c7 = smiles[i:i + 7]\n", " c8 = smiles[i:i + 8]\n", " c9 = smiles[i:i + 9]\n", " c10 = smiles[i:i + 10]\n", " c11 = smiles[i:i + 11]\n", "\n", " c12= smiles[i:i + 12]\n", "\n", " if c12 in self.table_12_chars:\n", " token.append(c12)\n", " i += 12\n", " continue\n", "\n", " if c11 in self.table_11_chars:\n", " token.append(c11)\n", " i += 11\n", " continue\n", "\n", " if c10 in self.table_10_chars:\n", " token.append(c10)\n", " i += 10\n", " continue\n", "\n", " if c9 in self.table_9_chars:\n", " token.append(c9)\n", " i += 9\n", " continue \n", " \n", " if c8 in self.table_8_chars:\n", " token.append(c8)\n", " i += 8\n", " continue\n", "\n", " if c7 in self.table_7_chars:\n", " token.append(c7)\n", " i += 7\n", " continue\n", "\n", " if c6 in self.table_6_chars:\n", " token.append(c6)\n", " i += 6\n", " continue\n", "\n", " if c5 in self.table_5_chars:\n", " token.append(c5)\n", " i += 5\n", " continue\n", "\n", " if c4 in self.table_4_chars:\n", " token.append(c4)\n", " i += 4\n", " continue\n", "\n", " if c3 in self.table_3_chars:\n", " token.append(c3)\n", " i += 3\n", " continue \n", "\n", "\n", " if c2 in self.table_2_chars:\n", " token.append(c2)\n", " i += 2\n", " continue\n", "\n", " if c1 in self.table_1_chars:\n", " token.append(c1)\n", " i += 1\n", " continue\n", "\n", "\n", "\n", " i += 1\n", " \n", " #print(token)\n", " return token\n", "\n", "\n", "\n", " def one_hot_encode(self, tokenized_smiles):\n", " result = np.array(\n", " [self.one_hot_dict[symbol] for symbol in tokenized_smiles],\n", " dtype=np.float32)\n", " result = result.reshape(1, result.shape[0], result.shape[1])\n", " ##print(result)\n", " return result\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9D0FvvsmywFk" }, "source": [ "## Data Loader" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N0m-AmjPufqR" }, "outputs": [], "source": [ "\n", "import json\n", "import os\n", "import numpy as np\n", "from tqdm import tqdm\n", "from tensorflow.keras.utils import Sequence\n", "#from lstm_chem.utils.smiles_tokenizer2 import SmilesTokenizer\n", "\n", "\n", "class DataLoader(Sequence):\n", " def __init__(self, config, data_type='train'):\n", " self.config = config\n", " self.data_type = data_type\n", " assert self.data_type in ['train', 'valid', 'finetune']\n", "\n", " self.max_len = 0\n", "\n", " if self.data_type == 'train':\n", " self.smiles = self._load(self.config.data_filename)\n", " elif self.data_type == 'finetune':\n", " self.smiles = self._load(self.config.finetune_data_filename)\n", " else:\n", " pass\n", "\n", " self.st = SmilesTokenizer()\n", " self.one_hot_dict = self.st.one_hot_dict\n", "\n", " self.tokenized_smiles = self._tokenize(self.smiles)\n", "\n", " if self.data_type in ['train', 'valid']:\n", " self.idx = np.arange(len(self.tokenized_smiles))\n", " self.valid_size = int(\n", " np.ceil(\n", " len(self.tokenized_smiles) * self.config.validation_split))\n", " np.random.seed(self.config.seed)\n", " np.random.shuffle(self.idx)\n", "\n", " def _set_data(self):\n", " if self.data_type == 'train':\n", " ret = [\n", " self.tokenized_smiles[self.idx[i]]\n", " for i in self.idx[self.valid_size:]\n", " ]\n", " elif self.data_type == 'valid':\n", " ret = [\n", " self.tokenized_smiles[self.idx[i]]\n", " for i in self.idx[:self.valid_size]\n", " ]\n", " else:\n", " ret = self.tokenized_smiles\n", " return ret\n", "\n", " def _load(self, data_filename):\n", " length = self.config.data_length\n", " print('loading SMILES...')\n", " with open(data_filename) as f:\n", " smiles = [s.rstrip() for s in f]\n", " if length != 0:\n", " smiles = smiles[:length]\n", " print('done.')\n", " return smiles\n", "\n", " def _tokenize(self, smiles):\n", " assert isinstance(smiles, list)\n", " print('tokenizing SMILES...')\n", " tokenized_smiles = [self.st.tokenize(smi) for smi in tqdm(smiles)]\n", "\n", " if self.data_type == 'train':\n", " for tokenized_smi in tokenized_smiles:\n", " length = len(tokenized_smi)\n", " if self.max_len < length:\n", " self.max_len = length\n", " self.config.train_smi_max_len = self.max_len\n", " print('done.')\n", " return tokenized_smiles\n", "\n", " def __len__(self):\n", " target_tokenized_smiles = self._set_data()\n", " if self.data_type in ['train', 'valid']:\n", " ret = int(\n", " np.ceil(\n", " len(target_tokenized_smiles) /\n", " float(self.config.batch_size)))\n", " else:\n", " ret = int(\n", " np.ceil(\n", " len(target_tokenized_smiles) /\n", " float(self.config.finetune_batch_size)))\n", " return ret\n", "\n", " def __getitem__(self, idx):\n", " target_tokenized_smiles = self._set_data()\n", " if self.data_type in ['train', 'valid']:\n", " data = target_tokenized_smiles[idx *\n", " self.config.batch_size:(idx + 1) *\n", " self.config.batch_size]\n", " else:\n", " data = target_tokenized_smiles[idx *\n", " self.config.finetune_batch_size:\n", " (idx + 1) *\n", " self.config.finetune_batch_size]\n", " data = self._padding(data)\n", "\n", " self.X, self.y = [], []\n", " for tp_smi in data:\n", " X = [self.one_hot_dict[symbol] for symbol in tp_smi[:-1]]\n", " self.X.append(X)\n", " y = [self.one_hot_dict[symbol] for symbol in tp_smi[1:]]\n", " self.y.append(y)\n", "\n", " self.X = np.array(self.X, dtype=np.float32)\n", " self.y = np.array(self.y, dtype=np.float32)\n", " \n", "# return self.X, self.y, [None]\n", " return self.X, self.y\n", "\n", " def _pad(self, tokenized_smi):\n", " return ['G'] + tokenized_smi + ['E'] + [\n", " 'A' for _ in range(self.max_len - len(tokenized_smi))\n", " ]\n", "\n", " def _padding(self, data):\n", " padded_smiles = [self._pad(t_smi) for t_smi in data]\n", " return padded_smiles" ] }, { "cell_type": "markdown", "metadata": { "id": "NbUu9dy63XKJ" }, "source": [ "## LSTM Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IymzAnUUvG1v" }, "outputs": [], "source": [ "import os\n", "import time\n", "from tensorflow.keras import Sequential\n", "from tensorflow.keras.models import model_from_json\n", "from tensorflow.keras.layers import LSTM, Dense\n", "from tensorflow.keras.initializers import RandomNormal\n", "\n", "\n", "\n", "class LSTMChem(object):\n", " def __init__(self, config, session='train'):\n", " assert session in ['train', 'generate', 'finetune'], \\\n", " 'one of {train, generate, finetune}'\n", "\n", " self.config = config\n", " self.session = session\n", " self.model = None\n", "\n", " if self.session == 'train':\n", " self.build_model()\n", " else:\n", " self.model = self.load(self.config.model_arch_filename,\n", " self.config.model_weight_filename)\n", "\n", " def build_model(self):\n", " st = SmilesTokenizer()\n", " n_table = len(st.table)\n", " weight_init = RandomNormal(mean=0.0,\n", " stddev=0.05,\n", " seed=self.config.seed)\n", "\n", " self.model = Sequential()\n", " self.model.add(\n", " LSTM(units=self.config.units,\n", " input_shape=(None, n_table),\n", " return_sequences=True,\n", " kernel_initializer=weight_init,\n", " dropout=0.1))\n", " self.model.add(\n", " LSTM(units=self.config.units,\n", " input_shape=(None, n_table),\n", " return_sequences=True,\n", " kernel_initializer=weight_init,\n", " dropout=0.2))\n", " self.model.add(\n", " Dense(units=n_table,\n", " activation='softmax',\n", " kernel_initializer=weight_init))\n", "\n", " arch = self.model.to_json(indent=2)\n", " self.config.model_arch_filename = os.path.join(self.config.exp_dir,\n", " 'model_arch.json')\n", " with open(self.config.model_arch_filename, 'w') as f:\n", " f.write(arch)\n", "\n", " self.model.compile(optimizer=self.config.optimizer,\n", " loss='categorical_crossentropy')\n", "\n", " def save(self, checkpoint_path):\n", " assert self.model, 'You have to build the model first.'\n", "\n", " print('Saving model ...')\n", " self.model.save_weights(checkpoint_path)\n", " print('model saved.')\n", "\n", " def load(self, model_arch_file, checkpoint_file):\n", " print(f'Loading model architecture from {model_arch_file} ...')\n", " with open(model_arch_file) as f:\n", " model = model_from_json(f.read())\n", " print(f'Loading model checkpoint from {checkpoint_file} ...')\n", " model.load_weights(checkpoint_file)\n", " print('Loaded the Model.')\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "GMH8I9iB3uSu" }, "source": [ "## Generator" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bltRLOMevG9D" }, "outputs": [], "source": [ "from tqdm import tqdm\n", "import numpy as np\n", "#from lstm_chem.utils.smiles_tokenizer2 import SmilesTokenizer\n", "\n", "\n", "class LSTMChemGenerator(object):\n", " def __init__(self, modeler):\n", " self.session = modeler.session\n", " self.model = modeler.model\n", " self.config = modeler.config\n", " self.st = SmilesTokenizer()\n", "\n", " def _generate(self, sequence):\n", " while (sequence[-1] != 'E') and (len(self.st.tokenize(sequence)) <=\n", " self.config.smiles_max_length):\n", " x = self.st.one_hot_encode(self.st.tokenize(sequence))\n", " preds = self.model.predict_on_batch(x)[0][-1]\n", " next_idx = self.sample_with_temp(preds)\n", " sequence += self.st.table[next_idx]\n", "\n", " sequence = sequence[1:].rstrip('E')\n", "\n", " return sequence\n", "\n", "\n", " def sample_with_temp(self, preds):\n", " streched = np.log(preds) / self.config.sampling_temp\n", " streched_probs = np.exp(streched) / np.sum(np.exp(streched))\n", " return np.random.choice(range(len(streched)), p=streched_probs)\n", "\n", " def sample(self, num=1, start='G'):\n", " sampled = []\n", " if self.session == 'generate':\n", " for _ in tqdm(range(num)):\n", " sampled.append(self._generate(start))\n", " return sampled\n", " else:\n", " from rdkit import Chem, RDLogger\n", " RDLogger.DisableLog('rdApp.*')\n", " while len(sampled) < num:\n", " sequence = self._generate(start)\n", " mol = Chem.MolFromSmiles(sequence)\n", " if mol is not None:\n", " canon_smiles = Chem.MolToSmiles(mol)\n", " sampled.append(canon_smiles)\n", " return sampled" ] }, { "cell_type": "markdown", "metadata": { "id": "PQuZ2lj-2y7g" }, "source": [ "## Fine Tuner" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XIL7Gx05vG55" }, "outputs": [], "source": [ "\n", "\n", "\n", "class LSTMChemFinetuner(LSTMChemGenerator):\n", " def __init__(self, modeler, finetune_data_loader):\n", " self.session = modeler.session\n", " self.model = modeler.model\n", " self.config = modeler.config\n", " self.finetune_data_loader = finetune_data_loader\n", " self.st = SmilesTokenizer()\n", "\n", " def finetune(self):\n", " self.model.compile(optimizer=self.config.optimizer,\n", " loss='categorical_crossentropy')\n", "\n", "# history = self.model.fit_generator(\n", " history = self.model.fit(\n", " self.finetune_data_loader,\n", " steps_per_epoch=self.finetune_data_loader.__len__(),\n", " epochs=self.config.finetune_epochs,\n", " verbose=self.config.verbose_training,\n", " use_multiprocessing=True,\n", " shuffle=True)\n", " return history\n" ] }, { "cell_type": "markdown", "metadata": { "id": "rPI7-OzS3bWd" }, "source": [ "## Trainer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bpmPDt1yvGqT" }, "outputs": [], "source": [ "from glob import glob\n", "import os\n", "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n", "\n", "\n", "class LSTMChemTrainer(object):\n", " def __init__(self, modeler, train_data_loader, valid_data_loader):\n", " self.model = modeler.model\n", " self.config = modeler.config\n", " self.train_data_loader = train_data_loader\n", " self.valid_data_loader = valid_data_loader\n", " self.callbacks = []\n", " self.init_callbacks()\n", "\n", " def init_callbacks(self):\n", " self.callbacks.append(\n", " ModelCheckpoint(\n", " filepath=os.path.join(\n", " self.config.checkpoint_dir,\n", " '%s-{epoch:02d}-{val_loss:.2f}.hdf5' %\n", " self.config.exp_name),\n", " monitor=self.config.checkpoint_monitor,\n", " mode=self.config.checkpoint_mode,\n", " save_best_only=self.config.checkpoint_save_best_only,\n", " save_weights_only=self.config.checkpoint_save_weights_only,\n", " verbose=self.config.checkpoint_verbose,\n", " ))\n", " self.callbacks.append(\n", " TensorBoard(\n", " log_dir=self.config.tensorboard_log_dir,\n", " write_graph=self.config.tensorboard_write_graph,\n", " ))\n", "\n", " def train(self):\n", "# history = self.model.fit_generator(\n", " history = self.model.fit(\n", " self.train_data_loader,\n", " steps_per_epoch=self.train_data_loader.__len__(),\n", " epochs=self.config.num_epochs,\n", " verbose=self.config.verbose_training,\n", " validation_data=self.valid_data_loader,\n", " validation_steps=self.valid_data_loader.__len__(),\n", " use_multiprocessing=True,\n", " shuffle=True,\n", " callbacks=self.callbacks)\n", "\n", " last_weight_file = glob(\n", " os.path.join(\n", " f'{self.config.checkpoint_dir}',\n", " f'{self.config.exp_name}-{self.config.num_epochs:02}*.hdf5')\n", " )[0]\n", "\n", " assert os.path.exists(last_weight_file)\n", " self.config.model_weight_filename = last_weight_file\n", "\n", " with open(os.path.join(self.config.exp_dir, 'config.json'), 'w') as f:\n", " f.write(self.config.toJSON(indent=2))" ] }, { "cell_type": "markdown", "metadata": { "id": "oUg2HPWy3n8K" }, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "id": "NVQQ-UBUvpZf", "outputId": "5595c9cc-a9d3-4c1d-c937-ad64e5aab838" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\"\\nfrom copy import copy\\n#from lstm_chem.utils.config import process_config\\n#from lstm_chem.utils.dirs import create_dirs\\n#from lstm_chem.data_loader import DataLoader\\n#from lstm_chem.model import LSTMChem\\n#from lstm_chem.trainer import LSTMChemTrainer\\n\\nCONFIG_FILE = '/content/base_config.json'\\n\\n\\ndef main():\\n config = process_config(CONFIG_FILE)\\n\\n # create the experiments dirs\\n create_dirs(\\n [config.exp_dir, config.tensorboard_log_dir, config.checkpoint_dir])\\n\\n print('Create the data generator.')\\n train_dl = DataLoader(config, data_type='train')\\n valid_dl = copy(train_dl)\\n valid_dl.data_type = 'valid'\\n\\n print('Create the model.')\\n modeler = LSTMChem(config, session='train')\\n\\n print('Create the trainer')\\n trainer = LSTMChemTrainer(modeler, train_dl, valid_dl)\\n\\n print('Start training the model.')\\n trainer.train()\\n\\n\\nif __name__ == '__main__':\\n main()\\n\\n\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 44 } ], "source": [ "'''\n", "from copy import copy\n", "#from lstm_chem.utils.config import process_config\n", "#from lstm_chem.utils.dirs import create_dirs\n", "#from lstm_chem.data_loader import DataLoader\n", "#from lstm_chem.model import LSTMChem\n", "#from lstm_chem.trainer import LSTMChemTrainer\n", "\n", "CONFIG_FILE = '/content/base_config.json'\n", "\n", "\n", "def main():\n", " config = process_config(CONFIG_FILE)\n", "\n", " # create the experiments dirs\n", " create_dirs(\n", " [config.exp_dir, config.tensorboard_log_dir, config.checkpoint_dir])\n", "\n", " print('Create the data generator.')\n", " train_dl = DataLoader(config, data_type='train')\n", " valid_dl = copy(train_dl)\n", " valid_dl.data_type = 'valid'\n", "\n", " print('Create the model.')\n", " modeler = LSTMChem(config, session='train')\n", "\n", " print('Create the trainer')\n", " trainer = LSTMChemTrainer(modeler, train_dl, valid_dl)\n", "\n", " print('Start training the model.')\n", " trainer.train()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()\n", "\n", "'''" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "HfusVbkepFFc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ " #!zip -r exp.zip experiments/" ], "metadata": { "id": "WYogVr1SYqIH" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#from google.colab import files\n", "#files.download(\"/content/exp.zip\")" ], "metadata": { "id": "AD3Sb9cUaW4K" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "u9o6Sxw14iN1" }, "source": [ "## Fine Tuning" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jiYkhYlk4wuc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2f4ee8a0-97ee-4e03-bc23-3cf6f610b067" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: rdkit in /usr/local/lib/python3.8/dist-packages (2022.9.5)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from rdkit) (1.22.4)\n", "Requirement already satisfied: Pillow in /usr/local/lib/python3.8/dist-packages (from rdkit) (8.4.0)\n" ] } ], "source": [ "pip install rdkit" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ESaAvgIV4gz8" }, "outputs": [], "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": null, "metadata": { "id": "RPVwkm9CvpV-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "72b02711-0176-4bef-9ecb-10c3503b271b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading model architecture from /content/drive/MyDrive/DDP FSR/experiments/2023-02-27/LSTM_Chem/model_arch.json ...\n", "Loading model checkpoint from /content/drive/MyDrive/DDP FSR/experiments/2023-02-27/LSTM_Chem/checkpoints/LSTM_Chem-04-0.44.hdf5 ...\n", "Loaded the Model.\n", "loading SMILES...\n", "done.\n", "tokenizing SMILES...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1567/1567 [00:03<00:00, 420.78it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "done.\n", "Epoch 1/15\n", "1567/1567 [==============================] - 30s 14ms/step - loss: 1.7070\n", "Epoch 2/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 1.4273\n", "Epoch 3/15\n", "1567/1567 [==============================] - 17s 11ms/step - loss: 1.3239\n", "Epoch 4/15\n", "1567/1567 [==============================] - 18s 12ms/step - loss: 1.2488\n", "Epoch 5/15\n", "1567/1567 [==============================] - 19s 12ms/step - loss: 1.2020\n", "Epoch 6/15\n", "1567/1567 [==============================] - 18s 12ms/step - loss: 1.1559\n", "Epoch 7/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 1.0998\n", "Epoch 8/15\n", "1567/1567 [==============================] - 19s 12ms/step - loss: 1.0606\n", "Epoch 9/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 1.0335\n", "Epoch 10/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 0.9963\n", "Epoch 11/15\n", "1567/1567 [==============================] - 19s 12ms/step - loss: 0.9668\n", "Epoch 12/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 0.9427\n", "Epoch 13/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 0.9174\n", "Epoch 14/15\n", "1567/1567 [==============================] - 21s 13ms/step - loss: 0.8950\n", "Epoch 15/15\n", "1567/1567 [==============================] - 18s 11ms/step - loss: 0.8713\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 49 } ], "source": [ "config = process_config('/content/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": null, "metadata": { "id": "CzkqEJsmvpRn" }, "outputs": [], "source": [ "finetuned_smiles = finetuner.sample(num=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NKkhyY_4-mS4", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e2920866-79b5-4e26-89d5-2dfc696d3f27" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['Cc1ccc2c(c1)C(C)C(CCCN(C)C)C1=C(C1)N2',\n", " 'CC(C)(C)C',\n", " 'CCCCCC',\n", " 'CN(C)CCON=C1c2ccccc2CCc2ccccc21',\n", " 'CC(C)(O)C(O)c1ccc(O)cc1',\n", " 'CCC',\n", " 'Cc1cc(O)ccc1C=C1[C]2CN(C)C2CC1=O',\n", " 'CCNc1cccc(C)c1C=Cc1ccccc1',\n", " 'CC(=O)c1cncn1CC1CCCCC1',\n", " 'CCCC(C)C1(C)C(=O)NC(=O)NC1=O',\n", " 'CCN(CC)C(=O)C1CCn2cnc1c2C(C)C',\n", " 'CC(C)C',\n", " 'C=C',\n", " 'NCCc1ccccc1',\n", " 'CNCCc1ccccn1',\n", " 'CN(C)CCON1c2ccccc2C=Cc2ccccc21',\n", " 'CN1C(=O)CC(c2ccccc2)C1=O',\n", " 'NC1=NC(=O)C1N(C(=O)C1=CC1)c1ccccc1',\n", " 'Cc1ccc2c(c1)C(c1ccccc1)=NCC(=O)N2C',\n", " 'NC(=O)NO',\n", " 'NC(=O)Nc1cccc(O)c1O',\n", " 'CN1C(=O)CCC1Cc1ccccc1',\n", " 'Cc1ccc2[nH]c(=O)nc(-c3ccccc3)c2c1',\n", " 'CCN1c2ccccc2Sc2ccccc21',\n", " 'CC(=C1CCC(=O)NC1=O)c1ccccc1',\n", " 'COc1ccc2c(c1)N(CC(C)CN(C)C)c1ccccc1S2',\n", " 'CCOC(=O)Oc1ccccc1C(C)C',\n", " 'C=C',\n", " 'CCCC(=O)C(=O)n1c2ccccc3sc3ccc(C)cc21',\n", " 'NC1OCC(c2ccccc2)=NC1=O',\n", " 'CCC',\n", " 'CC1CC(CNC(=O)c2cc(S(C)(=O)=O)ccc2N)N1C',\n", " 'C=C',\n", " 'CCc1ccccc1',\n", " 'CC1CCCCN1',\n", " 'O=C(O)Oc1cc2ccccc2s1',\n", " 'CCC(O)CC1(CC)C(=O)NC(=O)NC1=O',\n", " 'Cc1ccc2c(c1)N(C)CCC(C)C2=O',\n", " 'CC(C)(C)OC(=O)C(C)(C)c1ccccc1',\n", " 'CN(CCc1ccccc1)CCn1c(=O)[nH]c2ccccc21',\n", " 'CC(=O)NC(C)NNC(O)c1ccccc1',\n", " 'NC1CCCCCN1c1ccccc1',\n", " 'CCCN1CCN(c2cccc(C)c2C)CC1',\n", " 'C=C(C)OC(=O)O',\n", " 'O=C(O)C1CCN(c2ccccc2)C1',\n", " 'CN(C)CCC(N)N1c2ccccc2Sc2ccccc21',\n", " 'C[N+](C)(C)CCO',\n", " 'CCN(CC)CCOC(=O)c1ccc(NC2CCCCC2)cc1',\n", " 'CCC',\n", " 'NC(N)=O',\n", " 'CC(NN)c1ccccc1',\n", " 'CN1CCN(CCCCOC2c3ccccc3N(C)c3ccccc32)CC1',\n", " 'COc1cccc(C(O)(O)OC(C)C)c1',\n", " 'CN(C)CCC=C1c2ccccc2C(C)(C)c2ccccc21',\n", " 'CC1OC(c2cccc(Br)c2)CN(C)CCN1C',\n", " 'CCN1N=NNC(=O)C(c2ccccc2)=C1SS',\n", " 'Cc1cccc(C2=NCC(=O)N(C)c3ccc(C)cc32)c1',\n", " 'Cc1ccc2c(c1)N(CC(C)CN(C)C)c1ccccc1S2',\n", " 'CCCCCC',\n", " 'COC(=O)C1(c2ccccc2)NNC(=O)NC1=O',\n", " 'CC(C)C',\n", " 'Cc1ccc(CCN(C)C)cc1O',\n", " 'CCN1CCCC1CNC(=O)C1=C(C)C(C(O)N(CC)C(=O)OC(C)(C)O)=C1',\n", " 'CCOC(CC)CC(N)=O',\n", " 'CNS(=O)(=O)c1ccc2c(c1)N(CCC1CCN(CCOC)CC1)c1ccccc1S2',\n", " 'CN(C)c1cnccn1',\n", " 'CCC',\n", " 'CN1CCCC([C]([C]2CCCC2)c2ccccc2)CC1',\n", " 'CCC(=O)c1ccc(C)cc1',\n", " 'C=C',\n", " 'CC(=O)C1(O)CCCCC1',\n", " 'CN(C)c1cnccn1',\n", " 'Cc1ccc2c(c1)N(CCCN(C)C)c1ccccc1S2',\n", " 'CN1C(=S)CN=C(c2ccccc2)c2ccccc21',\n", " 'CN(C)CCC(=O)CCn1c2c(ccccc3ccccc31)S2',\n", " 'CC(N)C(=O)NNC(Br)c1ccncc1',\n", " 'CN(C)CCCc1c2c(nc3ccc(O)cc13)N2C',\n", " 'CC(=O)Nc1ccc(C)cc1',\n", " 'CC(C)C1CCC(=O)OC1(C)C',\n", " 'C=C',\n", " 'Cc1cccc(C)c1OCC1CCCN(C)C1',\n", " 'C=C',\n", " 'CCCCCCC',\n", " 'Cc1ccc2c(c1)C(c1ccccc1)=NCC(=O)N2C',\n", " 'Cc1ccc(C(=O)NCCN2CCN(c3ccccc3)CC2)cc1',\n", " 'CCn1ccc(=O)c(O)c1C',\n", " 'CCCCCCC',\n", " 'CCCCN1CCC(C(C)=O)CC1',\n", " 'CCN1C(=O)CN=C(CN2CCN(C)CC2)c2cc(Br)ccc21',\n", " 'CCOC(=O)c1ccc(O)cc1',\n", " 'CC(O)Cn1c2ccccc2n1C',\n", " 'CCC1(CC)C(=O)NC(=O)NC1=O',\n", " 'Cc1ccc2c(c1)C(c1ccccc1)=NCC(=S)N2CC(=O)O',\n", " 'Cc1ccc2c(c1)N(CC(C)CN(C)C)c1ccccc1S2',\n", " 'C1CCCCC1',\n", " 'NC(N)=Nc1nc(-c2ccccc2)cs1',\n", " 'Cc1cccc(C)c1NC1=NCC=C1',\n", " 'CN(C(=O)O)c1cc[nH]c(=O)c1-c1ccccc1',\n", " 'CN(C)CCc1ccccn1',\n", " 'Cc1cccc(C(=O)C(=O)N(C)C)c1']" ] }, "metadata": {}, "execution_count": 51 } ], "source": [ "finetuned_smiles\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gn4vlKNZ5IYR" }, "outputs": [], "source": [ "with open('/content/bbbp.txt') 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", " try:\n", " bv = AllChem.GetMACCSKeysFingerprint(mol)\n", " fp = np.zeros(len(bv))\n", " DataStructs.ConvertToNumpyArray(bv, fp)\n", " Kfps.append(fp)\n", " except:\n", " pass\n", "Klen = len(Kfps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1WvcJGzt5SFF" }, "outputs": [], "source": [ "with open('/content/bbbp.txt') 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", " try:\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", " except:\n", " pass\n", "Flen = len(Ffps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZGRvrTSL5aDL" }, "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)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uRgKLu425dkb" }, "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": null, "metadata": { "id": "726JRpW35e51", "colab": { "base_uri": "https://localhost:8080/", "height": 334 }, "outputId": "203eb081-ca40-499b-edb3-2f048b4f33ff" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "plt.figure(figsize=(8, 5))\n", "plt.scatter(X[:Klen, 0], X[:Klen, 1],\n", " c='w', edgecolors='k', label='Known E. Coli inhibitors')\n", "#plt.scatter(X[Klen:Klen + Flen, 0], X[Klen:Klen + Flen, 1],\n", "# s=200, c='r', marker='2', edgecolors='k', label='Iteration 4 Molecules')\n", "plt.scatter(X[Klen + Flen:, 0], X[Klen + Flen:, 1],\n", " c='b', marker='+', label='Finetuned Generated')\n", "plt.xlabel('PC 1')\n", "plt.ylabel('PC 2')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UhWJHYsJ5jC-" }, "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": null, "metadata": { "id": "RDIXRY0o9yOd" }, "outputs": [], "source": [ "showmols = []\n", "for i, j in zip(fmols, nsmols):\n", " showmols.append(i)\n", " showmols.append(j)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-FCC59yc1fiP", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "2a7697fb-a2d8-4f20-cde2-6387c1d939bd" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.8/dist-packages/rdkit/Chem/Draw/IPythonConsole.py:258: UserWarning: Truncating the list of molecules to be displayed to 50. Change the maxMols value to display more.\n", " warnings.warn(\n" ] }, { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "execution_count": 67 } ], "source": [ "\n", "Draw.MolsToGridImage(showmols, molsPerRow=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aMz-y3NWoYfc" }, "outputs": [], "source": [ "with open('bbbp_gen_fsr.txt', 'w') as f:\n", " for line in finetuned_smiles:\n", " f.write(f\"{line}\\n\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tclLlv8ot5zX", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ec2dea5d-5c81-435e-998f-3ff0ec70b1f1" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "100" ] }, "metadata": {}, "execution_count": 69 } ], "source": [ "len(finetuned_smiles)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F399xqZfQjF2" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }