Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
File size: 2,131 Bytes
6d1bbc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | """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()
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