"""Download DAVIS kinase binding dataset from GitHub. Source: https://github.com/dingyan20/Davis-Dataset-for-DTA-Prediction Files: drugs.csv (68 drugs), proteins.csv (442 kinases), drug_protein_affinity.csv (29,444 pairs with pKd values) Note: pytdc has dependency conflicts (requires rdkit<2024.3), so we download directly from the GitHub repository. """ from pathlib import Path import pandas as pd from negbiodb.download import download_file_http, load_config def main(): cfg = load_config() dl = cfg["downloads"]["davis"] base_url = dl["base_url"] files = dl["files"] dest_dir = Path(dl["dest_dir"]) min_rows = dl["min_rows"] dest_dir.mkdir(parents=True, exist_ok=True) print("=== DAVIS Dataset Download ===") print(f"Source: {base_url}") print(f"Dest: {dest_dir}") # Download each CSV file for fname in files: url = f"{base_url}/{fname}" dest = dest_dir / fname download_file_http(url, dest, desc=fname) # Load and merge into a single DataFrame drugs = pd.read_csv(dest_dir / "drugs.csv") proteins = pd.read_csv(dest_dir / "proteins.csv") affinities = pd.read_csv(dest_dir / "drug_protein_affinity.csv") print(f"\nDrugs: {len(drugs)} compounds") print(f"Proteins: {len(proteins)} kinases") print(f"Affinities: {len(affinities)} pairs") # Merge into a combined dataset merged = affinities.merge(drugs, on="Drug_Index").merge(proteins, on="Protein_Index") merged.to_parquet(dest_dir / "davis_merged.parquet", index=False) print(f"Merged: {len(merged)} rows -> davis_merged.parquet") if len(affinities) < min_rows: print(f"WARNING: Fewer rows than expected ({len(affinities)} < {min_rows})") # Basic statistics n_active = (affinities["Affinity"] > 5.0).sum() n_inactive = (affinities["Affinity"] <= 5.0).sum() print(f"\nActive (pKd > 5): {n_active}") print(f"Inactive (pKd <= 5): {n_inactive}") print(f"Active ratio: {n_active / len(affinities):.1%}") print("\nDAVIS download complete.") if __name__ == "__main__": main()