Target Druggability Screening

Compile a preliminary evidence dossier for a protein target. STRING associations and BindingDB measurements alone do not establish druggability or therapeutic relevance. This recipe retrieves a limited sample, not an exhaustive search.

Workflow

1. Confirm the target's UniProt accession and species. Resolve a gene name such as CDK4 through host.science.search("uniprot", ...) first; review ambiguous matches. BindingDB accepts UniProt accessions or PDB IDs, not arbitrary gene names. Use the same confirmed accession for both searches below.
2. Retrieve STRING interaction partners with an explicit species. The returned functional associations do not measure physical binding, whole-network hub centrality, or pathway enrichment.
3. Query BindingDB for one endpoint (Ki, Kd, IC50, or EC50) at a time. Even within an endpoint, review assay conditions before comparing values.
4. Use optional RDKit for molecular weight, calculated logP, HBD, HBA, and rotatable bonds. The Rule of Five uses the first four descriptors, allowing at most one violation. Install the existing chemistry extra in the active Python environment (uv sync --locked --extra chemistry) to enable this step. No RDKit, missing SMILES, and invalid structures produce Unknown, never invented descriptors or a passing verdict. Core imports remain stdlib-only.
5. Preserve qualified affinities (<, >, ~), missing values, citations, and repeated assay records. These are evidence, not distinct confirmed leads. Scores require an exact positive nM measurement and a complete descriptor screen; otherwise they remain Unscored. The score is an unvalidated within-endpoint heuristic, not a probability or a biological conclusion.
6. Save each complete query envelope with its provenance, then save the dossier with lineage to those exact artifact versions. Propagate connector failures; do not replace an error with an empty successful search.

Interactive / Cell Recipe

Run inside a persistent Python kernel cell. Host connectors mediate all network requests; the sidecar performs no direct networking.

import json
from pathlib import Path

from target_druggability_screening.kernel import format_dossier, rank_candidates

# Confirm both values before running: this example is human CDK4.
target = "P11802"
species = "9606"
affinity_type = "Ki"

ppi_res = host.science.search(
    "string", target, limit=10, filters={"species": species}
)
binding_res = host.science.search(
    "bindingdb", target, limit=20,
    filters={"cutoff": 100, "affinity_type": affinity_type},
)
ranked_leads = rank_candidates(
    binding_res["results"], top_k=10, affinity_type=affinity_type
)

source_versions = []
for result in (ppi_res, binding_res):
    source_path = Path(f"{target.lower()}-{result['database']}-source.json")
    source_path.write_text(json.dumps(result, indent=2, allow_nan=False), encoding="utf-8")
    saved = host.save_artifact(
        str(source_path), content_type="application/json", source=result["provenance"]
    )
    source_versions.append(saved["version_id"])

dossier_content = format_dossier(
    target=target,
    ppi_records=ppi_res["results"],
    ranked_leads=ranked_leads,
    provenance=[ppi_res["provenance"], binding_res["provenance"]],
)
dossier_path = Path(f"target-druggability-{target.lower()}.md")
dossier_path.write_text(dossier_content, encoding="utf-8")
artifact = host.save_artifact(
    str(dossier_path), content_type="text/markdown", input_version_ids=source_versions
)
print(f"Dossier saved as artifact version: {artifact['version_id']}")

Sidecar Functions

• calculate_smiles_descriptors(smiles): Parsed RDKit descriptors and engine version, or a reason for missing/invalid/unavailable chemistry.
• evaluate_lipinski(descriptors_or_smiles): Four-criterion screen; an incomplete input has pass_rule_of_5=None.
• score_lead(affinity_val, affinity_type, lipinski_pass, violations_count): Exact nM potency contributes 60/50/35/20/5 for <10/<100/<1000/<10000/>=10000. A complete Rule of Five screen contributes 40 (pass) or 15 (fail), minus 10 per violation, floored at zero. Unknown inputs return None.
• rank_candidates(ligands, top_k, affinity_type=...): Select one endpoint, rank scored assay records, and retain unscored evidence afterwards. Mixed endpoints without explicit selection raise an error. Repeated assays are retained, so row count is not a distinct-compound count.
• format_dossier(target, ppi_records, ranked_leads, provenance=...): Report evidence, unknowns, source links, retrieval hashes, and interpretation limits.

Descriptor definitions: RDKit Lipinski API and RDKit molecular descriptors.