drug-target-interaction

Drug-Target Interaction Prediction

A skill for computational prediction of drug-target interactions (DTI), covering molecular docking, machine learning-based binding affinity prediction, compound library screening, and target identification using cheminformatics and structural biology tools.

Drug-Target Interaction Databases

Key Data Resources

Fetching Bioactivity Data

from chembl_webresource_client.new_client import new_client

def get_target_bioactivities(target_chembl_id: str,
                              activity_type: str = "IC50",
                              max_nm: float = 10000) -> list[dict]:
    """
    Retrieve bioactivity data for a protein target from ChEMBL.
    Returns compounds with measured binding/inhibition values.
    """
    activity = new_client.activity
    results = activity.filter(
        target_chembl_id=target_chembl_id,
        standard_type=activity_type,
        standard_relation="=",
        standard_units="nM",
    ).only([
        "molecule_chembl_id", "canonical_smiles",
        "standard_value", "standard_type",
        "pchembl_value", "assay_description",
    ])

    filtered = []
    for r in results:
        if r.get("standard_value") and float(r["standard_value"]) <= max_nm:
            filtered.append({
                "molecule_id": r["molecule_chembl_id"],
                "smiles": r["canonical_smiles"],
                "activity_type": r["standard_type"],
                "value_nM": float(r["standard_value"]),
                "pchembl": float(r["pchembl_value"]) if r.get("pchembl_value") else None,
            })
    return filtered