{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "4a567878", "metadata": {}, "outputs": [], "source": [ "import requests\n", "class DownloadEnamine:\n", " \"\"\"\n", " This class is set up to download Enamine REAL database on a remote machine.\n", " Instatiation requires plain ``username`` and ``password``.\n", "\n", " .. code-block::python\n", " de = DownloadEnamine('foo.bar@baz.ac.uk', 'Foo123')\n", " de.download_all('REAL')\n", "\n", " Note, this is copied off the route of the web page and not the Enamine Store API.\n", " Plus the official documentation (emailed Word document) is for the old Store and\n", " no longer applies anyway (plain text username and password in GET header \"Authorization\").\n", "\n", " The URLs pointing to the download pages were copied off manually.\n", " \"\"\"\n", " REAL=[\n", " '2024.07_Enamine_REAL_HAC_25_1B_CXSMILES.cxsmiles.bz2',\n", " ]\n", " LOGIN_URL = 'https://enamine.net/compound-collections/real-compounds/real-database'\n", "\n", " def __init__(self, username, password):\n", " self.sesh = requests.Session()\n", " login_payload = {\n", " 'username': username,\n", " 'password': password,\n", " 'Submit': 'Login',\n", " 'remember': 'yes',\n", " 'option': 'com_users',\n", " 'task': 'user.login'\n", " }\n", " self.sesh.headers.update({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'})\n", " response = self.sesh.post(self.LOGIN_URL, data=login_payload)\n", " response.raise_for_status()\n", "\n", " print(\"Login appears successful.\")\n", "\n", " def download_all(self, catalogue='REAL'):\n", " \"\"\"\n", " The URLs of the databases files are in the class attribute of that same catalogue name (i.e. ``REAL``).\n", " \"\"\"\n", " for filename in getattr(self, catalogue):\n", " self.download('REAL', filename)\n", "\n", " def check(self, catalogue='REAL'):\n", " for filename in getattr(self, catalogue):\n", " with self.sesh.get(f'https://ftp.enamine.net/download/{catalogue}/{filename}', stream=True) as r:\n", " r.raise_for_status() # requests.exceptions.HTTPError\n", " for chunk in r.iter_content(chunk_size=8192):\n", " break\n", "\n", " def download(self, catalogue, filename):\n", " \"\"\"\n", " Downloads the ``filename`` of the given ``catalogue``\n", " \"\"\"\n", " with self.sesh.get(f'https://ftp.enamine.net/download/{catalogue}/{filename}', stream=True) as r:\n", " r.raise_for_status()\n", " with open(filename, 'wb') as f:\n", " for chunk in r.iter_content(chunk_size=8192):\n", " f.write(chunk)\n", "\n", "\n", "real_download = DownloadEnamine('taitdang@stanford.edu', 'Z!6CJd2BjQs!y4x')\n", "real_download.download_all('REAL')" ] }, { "cell_type": "code", "execution_count": null, "id": "c2cd8a3d", "metadata": {}, "outputs": [], "source": [ "!source /lfs/skampere1/0/sttruong/miniconda3/etc/profile.d/conda.sh" ] }, { "cell_type": "code", "execution_count": null, "id": "eceda207", "metadata": {}, "outputs": [], "source": [ "export LD_LIBRARY_PATH=\"/lfs/skampere1/0/sttruong/cheapvs_llm/Vina-GPU-2.1/AutoDock-Vina-GPU-2.1/boost_1_77_0/stage/lib:${LD_LIBRARY_PATH}\"" ] }, { "cell_type": "code", "execution_count": null, "id": "86fc4291", "metadata": {}, "outputs": [], "source": [ "conda install jupyter ipykernel\n", "python -m ipykernel install --user --name cheapvs_llm --display-name \"Python (cheapvs_llm)\"\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "56a50433", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading SMILES from: smiles_sampled_20k.txt\n", "Writing CSV with column 'SMILES' to: lig_llm.csv\n", "------------------------------\n", "------------------------------\n", "Processing finished.\n", "Successfully wrote: 20354 SMILES strings to CSV.\n", "Output CSV written to: lig_llm.csv\n" ] } ], "source": [ "import csv\n", "import os\n", "import sys\n", "\n", "# --- Configuration ---\n", "input_smiles_file = \"smiles_sampled_20k.txt\" # <-- Your input SMILES text file\n", "output_csv_file = \"lig_llm.csv\" # <-- Your desired output CSV filename\n", "csv_column_name = \"SMILES\" # <-- The header for the column in the CSV\n", "# --- End Configuration ---\n", "\n", "# --- Script Start ---\n", "\n", "# Check if input file exists\n", "if not os.path.isfile(input_smiles_file):\n", " print(f\"ERROR: Input file not found: {input_smiles_file}\")\n", " sys.exit(1)\n", "\n", "print(f\"Reading SMILES from: {input_smiles_file}\")\n", "print(f\"Writing CSV with column '{csv_column_name}' to: {output_csv_file}\")\n", "print(\"-\" * 30)\n", "\n", "count_success = 0\n", "count_skipped = 0\n", "\n", "try:\n", " # Open the input text file for reading ('r')\n", " # Open the output CSV file for writing ('w')\n", " # newline='' is important to prevent blank rows being inserted by csv.writer\n", " with open(input_smiles_file, 'r') as infile, \\\n", " open(output_csv_file, 'w', newline='') as outfile:\n", "\n", " # Create a CSV writer object\n", " csv_writer = csv.writer(outfile)\n", "\n", " # Write the header row\n", " csv_writer.writerow([csv_column_name])\n", "\n", " # Process each line in the input file\n", " for i, line in enumerate(infile):\n", " smiles_string = line.strip() # Remove leading/trailing whitespace (including newline)\n", "\n", " if not smiles_string: # Skip empty lines\n", " print(f\"Warning: Skipped empty line at line number {i+1}\")\n", " count_skipped += 1\n", " continue\n", "\n", " # Write the SMILES string as a row in the CSV\n", " # writerow expects an iterable (like a list), even for a single column\n", " csv_writer.writerow([smiles_string])\n", " count_success += 1\n", "\n", "except IOError as e:\n", " print(f\"ERROR: Could not open or write file. Details: {e}\")\n", " sys.exit(1)\n", "except Exception as e:\n", " # Catch any other unexpected errors during processing\n", " print(f\"An unexpected error occurred processing line {i+1}: {e}\")\n", " sys.exit(1)\n", "\n", "\n", "print(\"-\" * 30)\n", "print(f\"Processing finished.\")\n", "print(f\"Successfully wrote: {count_success} SMILES strings to CSV.\")\n", "if count_skipped > 0:\n", " print(f\"Skipped empty lines: {count_skipped}\")\n", "print(f\"Output CSV written to: {output_csv_file}\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "cdd74781", "metadata": {}, "outputs": [], "source": [ "from admet_ai import ADMETModel\n", "import numpy as np\n", "import pandas as pd\n", "from rdkit import Chem\n", "from rdkit.Chem import (\n", " QED,\n", " Crippen,\n", " Descriptors,\n", " Lipinski,\n", " MolSurf,\n", " RDConfig,\n", " AllChem,\n", " rdMolDescriptors as rdmd,\n", ")\n", "from rdkit.Chem.rdMolDescriptors import CalcFractionCSP3\n", "from rdkit.DataStructs import TanimotoSimilarity\n", "from scipy.stats import norm\n", "from pandarallel import pandarallel\n", "sys.path.append(os.path.join(RDConfig.RDContribDir, \"SA_Score\"))\n", "import sascorer\n", "\n", "def add_property_columns(df_in):\n", " pandarallel.initialize(progress_bar=False, nb_workers=12)\n", " model = ADMETModel(include_physchem=False, num_workers=12)\n", " \"\"\"Add property columns to the DataFrame using pandarallel.\"\"\"\n", " # Convert SMILES to RDKit Mol objects\n", " df_in[\"Mol\"] = df_in[\"SMILES\"].parallel_apply(lambda x: Chem.MolFromSmiles(x))\n", " AliphaticRings = Chem.MolFromSmarts(\"[$([A;R][!a])]\")\n", " df_in[\"MW\"] = df_in[\"Mol\"].parallel_apply(Descriptors.MolWt)\n", " df_in[\"LogP\"] = df_in[\"Mol\"].parallel_apply(Crippen.MolLogP)\n", " df_in[\"numHDonors\"] = df_in[\"Mol\"].parallel_apply(Lipinski.NumHDonors)\n", " df_in[\"numHAcceptors\"] = df_in[\"Mol\"].parallel_apply(Lipinski.NumHAcceptors)\n", " df_in[\"TPSA\"] = df_in[\"Mol\"].parallel_apply(MolSurf.TPSA)\n", " df_in[\"rotBonds\"] = df_in[\"Mol\"].parallel_apply(\n", " lambda mol: rdmd.CalcNumRotatableBonds(\n", " mol, rdmd.NumRotatableBondsOptions.Strict\n", " )\n", " )\n", " df_in[\"Arom\"] = df_in[\"Mol\"].parallel_apply(\n", " lambda mol: len(\n", " Chem.GetSSSR(Chem.DeleteSubstructs(Chem.Mol(mol), AliphaticRings))\n", " )\n", " )\n", " df_in[\"QED\"] = df_in[\"Mol\"].parallel_apply(QED.qed)\n", " df_in[\"SA\"] = df_in[\"Mol\"].parallel_apply(sascorer.calculateScore)\n", " df_in[\"FractionCSP3\"] = df_in[\"Mol\"].parallel_apply(\n", " lambda mol: CalcFractionCSP3(mol)\n", " )\n", "\n", " admet_predictions = model.predict(smiles=df_in[\"SMILES\"].values)\n", " properties = ['Lipinski', 'stereo_centers', 'AMES', 'BBB_Martins', 'Bioavailability_Ma', 'CYP1A2_Veith', 'CYP2C19_Veith', 'CYP2C9_Substrate_CarbonMangels', 'CYP2C9_Veith', 'CYP2D6_Substrate_CarbonMangels', 'CYP2D6_Veith', 'CYP3A4_Substrate_CarbonMangels', 'CYP3A4_Veith', 'Carcinogens_Lagunin', 'ClinTox', 'DILI', 'HIA_Hou', 'NR-AR-LBD', 'NR-AR', 'NR-AhR', 'NR-Aromatase', 'NR-ER-LBD', 'NR-ER', 'NR-PPAR-gamma', 'PAMPA_NCATS', 'Pgp_Broccatelli', 'SR-ARE', 'SR-ATAD5', 'SR-HSE', 'SR-MMP', 'SR-p53', 'Skin_Reaction', 'hERG', 'Caco2_Wang', 'Clearance_Hepatocyte_AZ', 'Clearance_Microsome_AZ', 'Half_Life_Obach', 'HydrationFreeEnergy_FreeSolv', 'LD50_Zhu', 'Lipophilicity_AstraZeneca', 'PPBR_AZ', 'Solubility_AqSolDB', 'VDss_Lombardo', 'Lipinski_drugbank_approved_percentile', 'stereo_centers_drugbank_approved_percentile', 'tpsa_drugbank_approved_percentile', 'AMES_drugbank_approved_percentile', 'BBB_Martins_drugbank_approved_percentile', 'Bioavailability_Ma_drugbank_approved_percentile', 'CYP1A2_Veith_drugbank_approved_percentile', 'CYP2C19_Veith_drugbank_approved_percentile', 'CYP2C9_Substrate_CarbonMangels_drugbank_approved_percentile', 'CYP2C9_Veith_drugbank_approved_percentile', 'CYP2D6_Substrate_CarbonMangels_drugbank_approved_percentile', 'CYP2D6_Veith_drugbank_approved_percentile', 'CYP3A4_Substrate_CarbonMangels_drugbank_approved_percentile', 'CYP3A4_Veith_drugbank_approved_percentile', 'Carcinogens_Lagunin_drugbank_approved_percentile', 'ClinTox_drugbank_approved_percentile', 'DILI_drugbank_approved_percentile', 'HIA_Hou_drugbank_approved_percentile', 'NR-AR-LBD_drugbank_approved_percentile', 'NR-AR_drugbank_approved_percentile', 'NR-AhR_drugbank_approved_percentile', 'NR-Aromatase_drugbank_approved_percentile', 'NR-ER-LBD_drugbank_approved_percentile', 'NR-ER_drugbank_approved_percentile', 'NR-PPAR-gamma_drugbank_approved_percentile', 'PAMPA_NCATS_drugbank_approved_percentile', 'Pgp_Broccatelli_drugbank_approved_percentile', 'SR-ARE_drugbank_approved_percentile', 'SR-ATAD5_drugbank_approved_percentile', 'SR-HSE_drugbank_approved_percentile', 'SR-MMP_drugbank_approved_percentile', 'SR-p53_drugbank_approved_percentile', 'Skin_Reaction_drugbank_approved_percentile', 'hERG_drugbank_approved_percentile', 'Caco2_Wang_drugbank_approved_percentile', 'Clearance_Hepatocyte_AZ_drugbank_approved_percentile', 'Clearance_Microsome_AZ_drugbank_approved_percentile', 'Half_Life_Obach_drugbank_approved_percentile', 'HydrationFreeEnergy_FreeSolv_drugbank_approved_percentile', 'LD50_Zhu_drugbank_approved_percentile', 'Lipophilicity_AstraZeneca_drugbank_approved_percentile', 'PPBR_AZ_drugbank_approved_percentile', 'Solubility_AqSolDB_drugbank_approved_percentile', 'VDss_Lombardo_drugbank_approved_percentile']\n", " for prop in properties:\n", " df_in[prop] = admet_predictions[prop].values\n", " # Apply rounding\n", " df_in = df_in.drop(columns=[\"Mol\"])\n", " cols = df_in.columns\n", " df_in[cols] = df_in[cols].round(3)\n", "\n", " return df_in\n", "\n", "\n", "def process_and_save_molecules(input_file, output_file):\n", " # Load input file\n", " df = (\n", " pd.read_csv(input_file)\n", " if input_file.endswith(\".csv\")\n", " else pd.read_parquet(input_file)\n", " )\n", "\n", " # Add molecular properties\n", " df = add_property_columns(df)\n", "\n", " # Save output file\n", " if output_file.endswith(\".csv\"):\n", " df.to_csv(output_file, index=False)\n", " elif output_file.endswith(\".parquet\"):\n", " df.to_parquet(output_file, index=False)\n", " else:\n", " raise ValueError(\"Output file must be .csv or .parquet\")\n", "\n", " print(f\"Processed DataFrame saved to: {output_file}\")\n", " \n", "# process_and_save_molecules('/lfs/skampere1/0/sttruong/cheapvs_llm/lig_llm.csv', '/lfs/skampere1/0/sttruong/cheapvs_llm/lig_llm_prop.csv')" ] }, { "cell_type": "code", "execution_count": 12, "id": "3c07c1c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Pandarallel will run on 12 workers.\n", "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/lfs/local/0/sttruong/miniconda3/envs/cheapvs_llm/lib/python3.10/site-packages/chemprop/utils.py:473: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " vars(torch.load(path, map_location=lambda storage, loc: storage)[\"args\"]),\n", "/lfs/local/0/sttruong/miniconda3/envs/cheapvs_llm/lib/python3.10/site-packages/chemprop/utils.py:112: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " state = torch.load(path, map_location=lambda storage, loc: storage)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/lfs/local/0/sttruong/miniconda3/envs/cheapvs_llm/lib/python3.10/site-packages/chemprop/utils.py:418: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " state = torch.load(path, map_location=lambda storage, loc: storage)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter \"readout.1.weight\".\n", "Loading pretrained parameter \"readout.1.bias\".\n", "Loading pretrained parameter \"readout.4.weight\".\n", "Loading pretrained parameter \"readout.4.bias\".\n", "Moving model to cuda\n", "Loading pretrained parameter \"encoder.encoder.0.cached_zero_vector\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_i.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_h.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.weight\".\n", "Loading pretrained parameter \"encoder.encoder.0.W_o.bias\".\n", "Loading pretrained parameter 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SMILESMWLogPnumHDonorsnumHAcceptorsTPSArotBondsAromQEDSA...Half_Life_Obach_drugbank_approved_percentileHydrationFreeEnergy_FreeSolv_drugbank_approved_percentileLD50_Zhu_drugbank_approved_percentileLipophilicity_AstraZeneca_drugbank_approved_percentilePPBR_AZ_drugbank_approved_percentileSolubility_AqSolDB_drugbank_approved_percentileVDss_Lombardo_drugbank_approved_percentileDrugBank IDNameDrug Groups
0COc1cc(C=Cc2nc(-c3ccccc3)cs2)ccc1OCC#N348.4274.8910555.14630.6362.317...15.43261.30338.03895.81292.5552.7924.769NaNNaNNaN
1COc1ccn2c(C=C(C#N)C(=O)c3ccc(Cl)cc3F)cnc2c1355.7563.9250567.39430.4042.755...62.38938.81384.87885.77087.8644.88684.839NaNNaNNaN
2C#CCOc1ccc(C=C(Cl)c2nc3ccc(C)cc3o2)cc1OC353.8054.8940444.49530.6122.612...79.64381.07832.76598.91492.2063.87796.472NaNNaNNaN
3CC(Sc1nnc(-c2ccccc2Cl)n1N)c1ncc(C(C)(C)C)o1377.9014.4511782.76430.5343.167...15.85965.76226.28993.29280.26417.91490.810NaNNaNNaN
4NC(=O)c1ccccc1OCC(=O)Nc1ccc(OC(F)F)c(Cl)c1370.7393.0582490.65720.7831.962...63.35835.16925.51478.63564.75410.2755.506NaNNaNNaN
..................................................................
20364COC1=CC=CC(NC2=C(C=NC3=C(C)C=C(C=C23)S(=O)(=O)...518.5953.92927131.69740.3792.571...96.85918.45760.60572.66465.56815.00612.214DB12137GSK-256066investigational
20365CS(=O)(=O)NC1=CC2=C(OC3=C2C(=CC=C3OC(F)F)C(=O)...516.3095.51326110.53640.3502.774...99.61226.87180.10969.29086.9336.08891.663DB12375Oglemilastinvestigational
20366[O-][N+]1=CC(=CC=C1)C#CC1=CC(=CC=C1)N1C=C(C(=O...422.4442.3111590.93340.3122.887...50.83434.12279.95363.97870.49231.21429.508DB13029MK-0873investigational
20367COC1=CC=C(C=C1OC1CCCC1)C(CC(N)=O)N1C(=O)C2=C(C...408.4543.2291598.93720.7092.776...37.37960.25651.10568.20574.5647.44588.212DB15640CDC-801investigational
20368OB1OCC2=C1C=CC(OC1=CC(C#N)=C(C=C1)C#N)=C2276.0601.4401586.27220.8382.894...10.23754.32391.12152.73473.05222.95524.157DB16039AN2898investigational
\n", "

20369 rows × 101 columns

\n", "" ], "text/plain": [ " SMILES MW LogP \\\n", "0 COc1cc(C=Cc2nc(-c3ccccc3)cs2)ccc1OCC#N 348.427 4.891 \n", "1 COc1ccn2c(C=C(C#N)C(=O)c3ccc(Cl)cc3F)cnc2c1 355.756 3.925 \n", "2 C#CCOc1ccc(C=C(Cl)c2nc3ccc(C)cc3o2)cc1OC 353.805 4.894 \n", "3 CC(Sc1nnc(-c2ccccc2Cl)n1N)c1ncc(C(C)(C)C)o1 377.901 4.451 \n", "4 NC(=O)c1ccccc1OCC(=O)Nc1ccc(OC(F)F)c(Cl)c1 370.739 3.058 \n", "... ... ... ... \n", "20364 COC1=CC=CC(NC2=C(C=NC3=C(C)C=C(C=C23)S(=O)(=O)... 518.595 3.929 \n", "20365 CS(=O)(=O)NC1=CC2=C(OC3=C2C(=CC=C3OC(F)F)C(=O)... 516.309 5.513 \n", "20366 [O-][N+]1=CC(=CC=C1)C#CC1=CC(=CC=C1)N1C=C(C(=O... 422.444 2.311 \n", "20367 COC1=CC=C(C=C1OC1CCCC1)C(CC(N)=O)N1C(=O)C2=C(C... 408.454 3.229 \n", "20368 OB1OCC2=C1C=CC(OC1=CC(C#N)=C(C=C1)C#N)=C2 276.060 1.440 \n", "\n", " numHDonors numHAcceptors TPSA rotBonds Arom QED SA ... \\\n", "0 0 5 55.14 6 3 0.636 2.317 ... \n", "1 0 5 67.39 4 3 0.404 2.755 ... \n", "2 0 4 44.49 5 3 0.612 2.612 ... \n", "3 1 7 82.76 4 3 0.534 3.167 ... \n", "4 2 4 90.65 7 2 0.783 1.962 ... \n", "... ... ... ... ... ... ... ... ... \n", "20364 2 7 131.69 7 4 0.379 2.571 ... \n", "20365 2 6 110.53 6 4 0.350 2.774 ... \n", "20366 1 5 90.93 3 4 0.312 2.887 ... \n", "20367 1 5 98.93 7 2 0.709 2.776 ... \n", "20368 1 5 86.27 2 2 0.838 2.894 ... \n", "\n", " Half_Life_Obach_drugbank_approved_percentile \\\n", "0 15.432 \n", "1 62.389 \n", "2 79.643 \n", "3 15.859 \n", "4 63.358 \n", "... ... \n", "20364 96.859 \n", "20365 99.612 \n", "20366 50.834 \n", "20367 37.379 \n", "20368 10.237 \n", "\n", " HydrationFreeEnergy_FreeSolv_drugbank_approved_percentile \\\n", "0 61.303 \n", "1 38.813 \n", "2 81.078 \n", "3 65.762 \n", "4 35.169 \n", "... ... \n", "20364 18.457 \n", "20365 26.871 \n", "20366 34.122 \n", "20367 60.256 \n", "20368 54.323 \n", "\n", " LD50_Zhu_drugbank_approved_percentile \\\n", "0 38.038 \n", "1 84.878 \n", "2 32.765 \n", "3 26.289 \n", "4 25.514 \n", "... ... \n", "20364 60.605 \n", "20365 80.109 \n", "20366 79.953 \n", "20367 51.105 \n", "20368 91.121 \n", "\n", " Lipophilicity_AstraZeneca_drugbank_approved_percentile \\\n", "0 95.812 \n", "1 85.770 \n", "2 98.914 \n", "3 93.292 \n", "4 78.635 \n", "... ... \n", "20364 72.664 \n", "20365 69.290 \n", "20366 63.978 \n", "20367 68.205 \n", "20368 52.734 \n", "\n", " PPBR_AZ_drugbank_approved_percentile \\\n", "0 92.555 \n", "1 87.864 \n", "2 92.206 \n", "3 80.264 \n", "4 64.754 \n", "... ... \n", "20364 65.568 \n", "20365 86.933 \n", "20366 70.492 \n", "20367 74.564 \n", "20368 73.052 \n", "\n", " Solubility_AqSolDB_drugbank_approved_percentile \\\n", "0 2.792 \n", "1 4.886 \n", "2 3.877 \n", "3 17.914 \n", "4 10.275 \n", "... ... \n", "20364 15.006 \n", "20365 6.088 \n", "20366 31.214 \n", "20367 7.445 \n", "20368 22.955 \n", "\n", " VDss_Lombardo_drugbank_approved_percentile DrugBank ID Name \\\n", "0 4.769 NaN NaN \n", "1 84.839 NaN NaN \n", "2 96.472 NaN NaN \n", "3 90.810 NaN NaN \n", "4 5.506 NaN NaN \n", "... ... ... ... \n", "20364 12.214 DB12137 GSK-256066 \n", "20365 91.663 DB12375 Oglemilast \n", "20366 29.508 DB13029 MK-0873 \n", "20367 88.212 DB15640 CDC-801 \n", "20368 24.157 DB16039 AN2898 \n", "\n", " Drug Groups \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "... ... \n", "20364 investigational \n", "20365 investigational \n", "20366 investigational \n", "20367 investigational \n", "20368 investigational \n", "\n", "[20369 rows x 101 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_df" ] }, { "cell_type": "code", "execution_count": null, "id": "975b13a2", "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }