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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standardizing Datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from rdkit import Chem\n",
"from rdkit.Chem import MolToSmiles\n",
"from scipy.stats import zscore\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"\n",
"def load_binding_affinity_dataset(csv_path, \n",
" protein_col_idx, \n",
" smiles_col_idx, \n",
" affinity_col_idx, \n",
" is_log10_affinity=True, \n",
" canonicalize_smiles=True, \n",
" affinity_unit=\"uM\",\n",
" delimiter=','):\n",
" \"\"\"\n",
" Load a protein-ligand binding affinity dataset and preprocess it.\n",
"\n",
" Args:\n",
" csv_path (str): Path to the CSV file.\n",
" protein_col_idx (int): Column index containing protein sequences.\n",
" smiles_col_idx (int): Column index containing molecule SMILES.\n",
" affinity_col_idx (int): Column index containing binding affinities.\n",
" is_log10_affinity (bool): Whether affinities are in log10. Default is True.\n",
" canonicalize_smiles (bool): Whether to canonicalize SMILES. Default is True.\n",
" delimiter (str): Delimiter for the CSV file. Default is ','.\n",
"\n",
" Returns:\n",
" pd.DataFrame: Processed DataFrame with columns \"seq\", \"smiles_can\",\n",
" \"affinity_uM\", \"neg_log10_affinity_M\", and \"affinity_norm\".\n",
" \"\"\"\n",
" # Load dataset\n",
" df = pd.read_csv(csv_path, delimiter=delimiter)\n",
"\n",
" # Extract relevant columns\n",
" df = df.iloc[:, [protein_col_idx, smiles_col_idx, affinity_col_idx]]\n",
" df.columns = [\"seq\", \"smiles\", \"affinity\"]\n",
"\n",
" # Canonicalize SMILES\n",
" if canonicalize_smiles:\n",
" def canonicalize(smiles):\n",
" try:\n",
" mol = Chem.MolFromSmiles(smiles)\n",
" return MolToSmiles(mol, canonical=True) if mol else None\n",
" except:\n",
" return None\n",
"\n",
" from tqdm import tqdm\n",
" tqdm.pandas()\n",
" df[\"smiles_can\"] = df[\"smiles\"].progress_apply(canonicalize)\n",
" df = df[df[\"smiles_can\"].notna()]\n",
" else:\n",
" df[\"smiles_can\"] = df[\"smiles\"]\n",
"\n",
" # Process affinities\n",
" if not is_log10_affinity:\n",
" # Convert plain Kd value to neg log10(M)\n",
" df[\"affinity_uM\"] = df[\"affinity\"]/(1e3 if affinity_unit == \"nM\" else 1)\n",
" \n",
" df[\"neg_log10_affinity_M\"] = -df[\"affinity_uM\"].apply(lambda x: np.log10(x/1e6) if x > 0 else np.nan)\n",
" else:\n",
" # Convert log10 values to plain uM for clarity\n",
" df[\"neg_log10_affinity_M\"] = df[\"affinity\"]\n",
" df[\"affinity_uM\"] = df[\"neg_log10_affinity_M\"].apply(lambda x: (10**(-x))*1e6)\n",
"\n",
" df.dropna(inplace=True)\n",
"\n",
" # Z-score normalization\n",
" df[\"affinity_norm\"] = zscore(df[\"neg_log10_affinity_M\"])\n",
"\n",
" # Select and reorder columns\n",
" df = df[[\"seq\", \"smiles_can\", \"affinity_uM\", \"neg_log10_affinity_M\", \"affinity_norm\"]]\n",
"\n",
" # Add normalization parameters as columns for reference\n",
" df[\"affinity_mean\"] = df[\"neg_log10_affinity_M\"].mean()\n",
" df[\"affinity_std\"] = df[\"neg_log10_affinity_M\"].std()\n",
"\n",
" return df.sort_values(by=\"affinity_norm\", ascending=False)\n",
"\n",
"dataset = load_binding_affinity_dataset(\n",
" csv_path=\"data/raw_data/bindingdb_ic50.csv\",\n",
" protein_col_idx=3,\n",
" smiles_col_idx=1,\n",
" affinity_col_idx=4,\n",
" is_log10_affinity=False, # Specify if Kd values are plain\n",
" canonicalize_smiles=True,\n",
" affinity_unit=\"nM\",\n",
" delimiter=\",\"\n",
")\n",
"dataset.to_parquet(\"data/bindingdb-ic50.parquet\", index=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TDC Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from rdkit import Chem\n",
"from tdc.multi_pred import DTI\n",
"\n",
"def process_dataset(name):\n",
" data = DTI(name=name)\n",
" data.harmonize_affinities(mode='mean')\n",
" data.convert_to_log()\n",
" df = data.get_data()\n",
" df['smiles_can'] = df['Drug'].apply(lambda s: Chem.MolToSmiles(Chem.MolFromSmiles(s), isomericSmiles=True, canonical=True) if Chem.MolFromSmiles(s) else None)\n",
" return df[['smiles_can', 'Target', 'Y']].dropna(subset=['smiles_can']).rename(columns={'Target': 'seq', 'Y': 'neg_log_10_affinity'})\n",
"\n",
"datasets = ['BindingDB_Ki', 'BindingDB_Kd', 'BindingDB_IC50', 'DAVIS', 'KIBA']\n",
"processed_data = [process_dataset(name) for name in datasets]\n",
"\n",
"binding_db = pd.concat(processed_data[:3]).drop_duplicates().reset_index(drop=True)\n",
"binding_db.to_csv(\"data/bindingdb.csv\", index=False)\n",
"processed_data[3].to_csv(\"data/davis.csv\", index=False)\n",
"processed_data[4].to_csv(\"data/kiba.csv\", index=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PDBbind"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from pathlib import Path\n",
"from Bio import PDB\n",
"from Bio.PDB.Polypeptide import PPBuilder\n",
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"\n",
"ppb = PPBuilder()\n",
"\n",
"def get_protein_sequence(structure):\n",
" \"\"\"Extract protein sequence from a PDB structure.\"\"\"\n",
" sequence = \"\"\n",
" for pp in ppb.build_peptides(structure):\n",
" sequence += str(pp.get_sequence())\n",
" return sequence\n",
"\n",
"def get_canonical_smiles(mol):\n",
" \"\"\"Convert RDKit molecule to canonical SMILES.\"\"\"\n",
" return Chem.MolToSmiles(mol, isomericSmiles=True, canonical=True)\n",
"\n",
"def process_pdbbind_data(pdbbind_dir, index_file):\n",
" pdbbind_dir = Path(pdbbind_dir).expanduser()\n",
" parser = PDB.PDBParser(QUIET=True)\n",
" data = []\n",
" data_ic50 = []\n",
" data_ki = []\n",
" data_kd = []\n",
" data_equal_only = []\n",
" data_ic50_equal_only = []\n",
" data_ki_equal_only = []\n",
" data_kd_equal_only = []\n",
"\n",
" # Read the index file\n",
" df_index = pd.read_csv(index_file, sep='\\s+', header=None, comment= \"#\", usecols=[0,1,2,3,4,6,7],\n",
" names=['PDB_ID', 'Resolution', 'Release_Year', '-logKd/Ki', 'Kd/Ki', 'Reference', 'Ligand_Name'])\n",
"\n",
" # Get the total number of entries for progress tracking\n",
" total_entries = len(df_index)\n",
"\n",
" # Use tqdm for progress tracking\n",
" with tqdm(total=total_entries, desc=\"Processing PDBbind data\") as pbar:\n",
" # Create separate lists for different binding types\n",
" \n",
" for _, row in df_index.iterrows():\n",
" pdb_id = row['PDB_ID']\n",
" subdir = pdbbind_dir / pdb_id\n",
" kd_ki_value = row['Kd/Ki']\n",
"\n",
" if subdir.is_dir():\n",
" # Process protein\n",
" protein_file = subdir / f\"{pdb_id}_protein.pdb\"\n",
" if protein_file.exists():\n",
" structure = parser.get_structure(pdb_id, protein_file)\n",
" sequence = get_protein_sequence(structure)\n",
"\n",
" # Process ligand\n",
" ligand_file = subdir / f\"{pdb_id}_ligand.mol2\"\n",
" if ligand_file.exists():\n",
" mol = Chem.MolFromMol2File(str(ligand_file))\n",
" if mol is not None:\n",
" smiles = get_canonical_smiles(mol)\n",
"\n",
" # Get binding affinity\n",
" neg_log_10_affinity = row['-logKd/Ki']\n",
"\n",
" # Create entry dictionary\n",
" entry = {\n",
" 'smiles_can': smiles,\n",
" 'seq': sequence,\n",
" 'neg_log10_affinity_M': neg_log_10_affinity,\n",
" 'affinity_uM': 10**(6-neg_log_10_affinity)\n",
" }\n",
" \n",
" # Add to appropriate list based on binding type\n",
" data.append(entry) # Add to the combined list\n",
" \n",
" if kd_ki_value.startswith('IC50'):\n",
" data_ic50.append(entry)\n",
" if \"=\" in kd_ki_value:\n",
" data_ic50_equal_only.append(entry)\n",
" data_equal_only.append(entry)\n",
" elif kd_ki_value.startswith('Ki'):\n",
" data_ki.append(entry)\n",
" if \"=\" in kd_ki_value:\n",
" data_ic50_equal_only.append(entry)\n",
" data_equal_only.append(entry)\n",
" elif kd_ki_value.startswith('Kd'):\n",
" data_kd.append(entry)\n",
" if \"=\" in kd_ki_value:\n",
" data_ic50_equal_only.append(entry)\n",
" data_equal_only.append(entry)\n",
"\n",
"\n",
" pbar.update(1)\n",
"\n",
" # Create dataframes for each binding type\n",
" df_all = pd.DataFrame(data)\n",
" df_ic50 = pd.DataFrame(data_ic50)\n",
" df_ki = pd.DataFrame(data_ki)\n",
" df_kd = pd.DataFrame(data_kd)\n",
" df_all_equal_only = pd.DataFrame(data)\n",
" df_ic50_equal_only = pd.DataFrame(data_ic50_equal_only)\n",
" df_ki_equal_only = pd.DataFrame(data_ki_equal_only)\n",
" df_kd_equal_only = pd.DataFrame(data_kd_equal_only)\n",
" \n",
" return df_all, df_ic50, df_ki, df_kd, df_all_equal_only, df_ic50_equal_only, df_ki_equal_only, df_kd_equal_only\n",
"\n",
"# Process data from PDBbind refined set\n",
"pdbbind_refined_dir = \"/Users/tyler/Downloads/refined-set\"\n",
"index_refined_file = \"/Users/tyler/Downloads/PDBbind_v2020_plain_text_index/index/INDEX_refined_data.2020\"\n",
"df_refined_all, df_refined_ic50, df_refined_ki, df_refined_kd, df_refined_all_equal_only, df_refined_ic50_equal_only, df_refined_ki_equal_only, df_refined_kd_equal_only = process_pdbbind_data(pdbbind_refined_dir, index_refined_file)\n",
"\n",
"df_refined_all.to_parquet('pdbbind-2020-refined.parquet', index=False)\n",
"df_refined_ki.to_parquet('pdbbind-2020-refined-ki.parquet', index=False)\n",
"df_refined_kd.to_parquet('pdbbind-2020-refined-kd.parquet', index=False)\n",
"\n",
"# Process data from PDBbind general set\n",
"pdbbind_general_dir = \"/Users/tyler/Downloads/v2020-other-PL\"\n",
"index_general_file = \"/Users/tyler/Downloads/PDBbind_v2020_plain_text_index/index/INDEX_general_PL_data.2020\"\n",
"df_general_all, df_general_ic50, df_general_ki, df_general_kd, df_general_all_equal_only, df_general_ic50_equal_only, df_general_ki_equal_only, df_general_kd_equal_only = process_pdbbind_data(pdbbind_general_dir, index_general_file)\n",
"df_general_all.to_parquet('pdbbind-2020-general.parquet', index=False)\n",
"df_general_ic50.to_parquet('pdbbind-2020-general-ic50.parquet', index=False)\n",
"df_general_ki.to_parquet('pdbbind-2020-general-ki.parquet', index=False)\n",
"df_general_kd.to_parquet('pdbbind-2020-general-kd.parquet', index=False)\n",
"df_general_all_equal_only.to_parquet('pdbbind-2020-general-all-equal-only.parquet', index=False)\n",
"df_general_ic50_equal_only.to_parquet('pdbbind-2020-general-ic50-equal-only.parquet', index=False)\n",
"df_general_ki_equal_only.to_parquet('pdbbind-2020-general-ki-equal-only.parquet', index=False)\n",
"df_general_kd_equal_only.to_parquet('pdbbind-2020-general-kd-equal-only.parquet', index=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Combined set size: 19280\n",
"Refined set size: 5312\n",
"General set size: 13941\n"
]
}
],
"source": [
"# Read the combined and refined datasets\n",
"pdbbind_combined = pd.read_parquet('pdbbind-2020-combined.parquet')\n",
"pdbbind_refined = pd.read_parquet('pdbbind-2020-refined.parquet')\n",
"\n",
"# Find rows in combined that are not in refined by comparing seq and smiles_can pairs\n",
"general_set = pdbbind_combined[~pdbbind_combined.set_index(['seq', 'smiles_can']).index.isin(\n",
" pdbbind_refined.set_index(['seq', 'smiles_can']).index\n",
")].reset_index(drop=True)\n",
"\n",
"print(f\"Combined set size: {len(pdbbind_combined)}\")\n",
"print(f\"Refined set size: {len(pdbbind_refined)}\")\n",
"print(f\"General set size: {len(general_set)}\")\n",
"\n",
"# Save the general set\n",
"general_set.to_parquet('pdbbind-2020-general.parquet', index=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering bindingdb-kd"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mode of neg_log10_affinity_M in BindingDB IC50 dataset: 5.00\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of entries after filtering: 35845\n"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from statistics import mode\n",
"# Read the bindingdb-ic50 dataset\n",
"bindingdb_df = pd.read_csv('bindingdb-kd.csv')\n",
"\n",
"# Calculate the mode of neg_log10_affinity_M\n",
"mode_affinity = mode([x for x in bindingdb_df['neg_log10_affinity_M'].tolist()])\n",
"# Visualize before filtering\n",
"plt.hist(bindingdb_df['neg_log10_affinity_M'], bins=30, alpha=0.7)\n",
"plt.title('Before Filtering')\n",
"plt.show()\n",
"\n",
"print(f\"Mode of neg_log10_affinity_M in BindingDB IC50 dataset: {mode_affinity:.2f}\")\n",
"\n",
"# set seed\n",
"np.random.seed(42)\n",
"# Filter out 90% of rows where neg_log10_affinity_M equals 5\n",
"mask_value_5 = bindingdb_df['neg_log10_affinity_M'] == 5\n",
"rows_to_keep = ~mask_value_5 | (mask_value_5 & (np.random.rand(len(bindingdb_df)) < 0.05))\n",
"bindingdb_df = bindingdb_df[rows_to_keep].reset_index(drop=True)\n",
"\n",
"# Visualize after filtering\n",
"plt.hist(bindingdb_df['neg_log10_affinity_M'], bins=30, alpha=0.7)\n",
"plt.title('After Filtering')\n",
"plt.show()\n",
"\n",
"print(f\"Number of entries after filtering: {len(bindingdb_df)}\")\n",
"\n",
"bindingdb_df.to_csv('bindingdb-kd-filtered.csv', index=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converted bindingdb-ki.csv to bindingdb-ki.parquet\n",
"Converted pdbbind-2020-refined.csv to pdbbind-2020-refined.parquet\n",
"Converted davis-filtered.csv to davis-filtered.parquet\n",
"Converted bindingdb-kd-filtered.csv to bindingdb-kd-filtered.parquet\n",
"Converted glaser.csv to glaser.parquet\n",
"Converted bindingdb-ic50.csv to bindingdb-ic50.parquet\n",
"Converted pdbbind-2020-combined.csv to pdbbind-2020-combined.parquet\n",
"Converted bindingdb-kd.csv to bindingdb-kd.parquet\n",
"Converted davis.csv to davis.parquet\n",
"Converted kiba.csv to kiba.parquet\n"
]
}
],
"source": [
"import pandas as pd\n",
"import glob\n",
"\n",
"# Get all CSV files in current directory\n",
"csv_files = glob.glob('*.csv')\n",
"\n",
"# Convert each CSV to parquet\n",
"for csv_file in csv_files:\n",
" # Read CSV\n",
" df = pd.read_csv(csv_file)\n",
" \n",
" # Create parquet filename by replacing .csv extension\n",
" parquet_file = csv_file.replace('.csv', '.parquet')\n",
" \n",
" # Save as parquet\n",
" df.to_parquet(parquet_file, index=False)\n",
" print(f\"Converted {csv_file} to {parquet_file}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean affinity for glaser.parquet: 6.51\n",
"Mean affinity for bindingdb-ki.parquet: 6.88\n",
"Mean affinity for train.parquet: 6.46\n",
"Mean affinity for kiba.parquet: 7.93\n",
"Mean affinity for bindingdb-kd-filtered.parquet: 6.50\n",
"Mean affinity for pdbbind-2020-combined.parquet: 6.36\n",
"Mean affinity for pdbbind-2020-refined.parquet: 6.39\n",
"Mean affinity for test_25_targets_40_percent_similarity.parquet: 5.69\n",
"Mean affinity for test_1000_drugs.parquet: 6.38\n",
"Mean affinity for davis-filtered.parquet: 6.49\n",
"Mean affinity for bindingdb-ic50.parquet: 6.37\n",
"Mean affinity for test_25_targets_80_percent_similarity.parquet: 5.73\n",
"Mean affinity for bindingdb-kd.parquet: 5.81\n",
"Mean affinity for davis.parquet: 5.41\n",
"Mean affinity for test_25_targets_60_percent_similarity.parquet: 6.75\n",
"Mean affinity for affinity-data-combined.parquet: 6.46\n"
]
}
],
"source": [
"import glob\n",
"import pandas as pd\n",
"# Print mean affinity values for each dataset\n",
"parquet_files = glob.glob('*.parquet')\n",
"\n",
"for parquet_file in parquet_files:\n",
" try:\n",
" df = pd.read_parquet(parquet_file)\n",
" if 'neg_log10_affinity_M' in df.columns:\n",
" mean_affinity = df['neg_log10_affinity_M'].mean()\n",
" print(f\"Mean affinity for {parquet_file}: {mean_affinity:.2f}\")\n",
" except Exception as e:\n",
" print(f\"Could not process {parquet_file}: {str(e)}\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Combined dataset statistics:\n",
"Mean affinity: 6.5416\n",
"Standard deviation: 1.5625\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Load the combined dataset\n",
"df = pd.read_parquet('affinity-data-combined.parquet')\n",
"\n",
"# Calculate mean and std dev\n",
"mean_affinity = df['neg_log10_affinity_M'].mean()\n",
"std_affinity = df['neg_log10_affinity_M'].std()\n",
"\n",
"print(f\"Combined dataset statistics:\")\n",
"print(f\"Mean affinity: {mean_affinity:.4f}\")\n",
"print(f\"Standard deviation: {std_affinity:.4f}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
"nbformat_minor": 2
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|