scientific-skills/Evidence Insights/chembl-database/SKILL.md
Query the ChEMBL database for bioactive molecules, targets, bioactivities, and approved drugs; use this when you need to filter by physicochemical properties (e.g., MW, LogP), chemical structure (SMILES), or retrieve drug mechanism information.
npx skillsauth add aipoch/medical-research-skills chembl-databaseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
4 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
chembl_webresource_client (latest available via pip/uv)Install:
uv pip install chembl_webresource_client
Additional references (optional, if present in this repository):
references/api_reference.md (filter syntax and resource list)scripts/query_chembl.py (CLI wrapper example)from chembl_webresource_client.new_client import new_client
def main():
molecule = new_client.molecule
target = new_client.target
activity = new_client.activity
mechanism = new_client.mechanism
# 1) Search for molecules by name (case-insensitive substring match)
mols = list(molecule.filter(pref_name__icontains="aspirin")[:5])
if not mols:
raise SystemExit("No molecules found for query.")
first = mols[0]
chembl_id = first.get("molecule_chembl_id")
print("Top molecule hit:", chembl_id, "-", first.get("pref_name"))
# 2) Filter molecules by a simple property constraint (example: MW <= 500)
# Note: exact field names and operators depend on ChEMBL API schema.
druglike = list(molecule.filter(molecule_properties__mw_freebase__lte=500)[:5])
print("Example drug-like hits (MW<=500):", [m.get("molecule_chembl_id") for m in druglike])
# 3) Get target information (example: targets containing "COX")
targets = list(target.filter(pref_name__icontains="cyclooxygenase")[:5])
print("Example targets:", [(t.get("target_chembl_id"), t.get("pref_name")) for t in targets])
# 4) Query bioactivity for a molecule (IC50/Ki/EC50 etc. depend on available records)
# Here we fetch a few activity records linked to the molecule.
acts = list(activity.filter(molecule_chembl_id=chembl_id)[:5])
for a in acts:
print(
"Activity:",
a.get("activity_id"),
"type=", a.get("standard_type"),
"value=", a.get("standard_value"),
"units=", a.get("standard_units"),
"target=", a.get("target_chembl_id"),
)
# 5) Retrieve mechanism-of-action records (often used for approved drugs)
mechs = list(mechanism.filter(molecule_chembl_id=chembl_id)[:5])
for m in mechs:
print(
"Mechanism:",
"target=", m.get("target_chembl_id"),
"action=", m.get("action_type"),
"mechanism=", m.get("mechanism_of_action"),
)
if __name__ == "__main__":
main()
chembl_webresource_client.new_client.new_client to access resource endpoints such as molecule, target, activity, and mechanism..filter(...) with field lookups and operators (e.g., __icontains, __lte). The exact available fields and supported operators are defined by the ChEMBL API schema; consult references/api_reference.md for the authoritative list and examples.[:5]) to limit network calls and output size.standard_type, standard_value, and standard_units. Not all records contain all fields; code should handle missing keys.mechanism resource and is typically most complete for approved/annotated drugs.references/api_reference.md.tools
Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
development
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
tools
Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.
testing
Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.