drug-discovery

name drug-discovery description Drug discovery: ChEMBL search, drug-likeness, interactions. platforms ["linux","macos","windows"] version 1.0.0 author bennytimz license MIT metadata {"hermes":{"tags":["science","chemistry","pharmacology","research","health"]}} prerequisites {"commands":["curl","python"]} Drug Discovery & Pharmaceutical Research You are an expert pharmaceutical scientist and medicinal chemist with deep knowledge of drug discovery, cheminformatics, and clinical pharmacology. Use this skill for all pharma/chemistry research tasks. Core Workflows 1 — Bioactive Compound Search (ChEMBL) Search ChEMBL (the world's largest open bioactivity database) for compounds by target, activity, or molecule name. No API key required. # Search compounds by target name (e.g. "EGFR", "COX-2", "ACE") TARGET= " $1 " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $TARGET " ) curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q= ${ENCODED} &format=json" \ | python -c " import json,sys data=json.load(sys.stdin) targets=data.get('targets',[])[:5] for t in targets: print(f\"ChEMBL ID : {t.get('target_chembl_id')}\") print(f\"Name : {t.get('pref_name')}\") print(f\"Type : {t.get('target_type')}\") print() " # Get bioactivity data for a ChEMBL target ID TARGET_ID= " $1 " # e.g. CHEMBL203 curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id= ${TARGET_ID} &pchembl_value__gte=6&limit=10&format=json" \ | python -c " import json,sys data=json.load(sys.stdin) acts=data.get('activities',[]) print(f'Found {len(acts)} activities (pChEMBL >= 6):') for a in acts: print(f\" Molecule: {a.get('molecule_chembl_id')} | {a.get('standard_type')}: {a.get('standard_value')} {a.get('standard_units')} | pChEMBL: {a.get('pchembl_value')}\") " # Look up a specific molecule by ChEMBL ID MOL_ID= " $1 " # e.g. CHEMBL25 (aspirin) curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/ ${MOL_ID} ?format=json" \ | python -c " import json,sys m=json.load(sys.stdin) props=m.get('molecule_properties',{}) or {} print(f\"Name : {m.get('pref_name','N/A')}\") print(f\"SMILES : {m.get('molecule_structures',{}).get('canonical_smiles','N/A') if m.get('molecule_structures') else 'N/A'}\") print(f\"MW : {props.get('full_mwt','N/A')} Da\") print(f\"LogP : {props.get('alogp','N/A')}\") print(f\"HBD : {props.get('hbd','N/A')}\") print(f\"HBA : {props.get('hba','N/A')}\") print(f\"TPSA : {props.get('psa','N/A')} Ų\") print(f\"Ro5 violations: {props.get('num_ro5_violations','N/A')}\") print(f\"QED : {props.get('qed_weighted','N/A')}\") " 2 — Drug-Likeness Calculation (Lipinski Ro5 + Veber) Assess any molecule against established oral bioavailability rules using PubChem's free property API — no RDKit install needed. COMPOUND= " $1 " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $COMPOUND " ) curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/ ${ENCODED} /property/MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,RotatableBondCount,TPSA,InChIKey/JSON" \ | python -c " import json,sys data=json.load(sys.stdin) props=data['PropertyTable']['Properties'][0] mw = float(props.get('MolecularWeight', 0)) logp = float(props.get('XLogP', 0)) hbd = int(props.get('HBondDonorCount', 0)) hba = int(props.get('HBondAcceptorCount', 0)) rot = int(props.get('RotatableBondCount', 0)) tpsa = float(props.get('TPSA', 0)) print('=== Lipinski Rule of Five (Ro5) ===') print(f' MW {mw:.1f} Da {\"✓\" if mw<=500 else \"✗ VIOLATION (>500)\"}') print(f' LogP {logp:.2f} {\"✓\" if logp<=5 else \"✗ VIOLATION (>5)\"}') print(f' HBD {hbd} {\"✓\" if hbd<=5 else \"✗ VIOLATION (>5)\"}') print(f' HBA {hba} {\"✓\" if hba<=10 else \"✗ VIOLATION (>10)\"}') viol = sum([mw>500, logp>5, hbd>5, hba>10]) print(f' Violations: {viol}/4 {\"→ Likely orally bioavailable\" if viol<=1 else \"→ Poor oral bioavailability predicted\"}') print() print('=== Veber Oral Bioavailability Rules ===') print(f' TPSA {tpsa:.1f} Ų {\"✓\" if tpsa<=140 else \"✗ VIOLATION (>140)\"}') print(f' Rot. bonds {rot} {\"✓\" if rot<=10 else \"✗ VIOLATION (>10)\"}') print(f' Both rules met: {\"Yes → good oral absorption predicted\" if tpsa<=140 and rot<=10 else \"No → reduced oral absorption\"}') " 3 — Drug Interaction & Safety Lookup (OpenFDA) DRUG= " $1 " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $DRUG " ) curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\" ${ENCODED} \"&limit=3" \ | python -c " import json,sys data=json.load(sys.stdin) results=data.get('results',[]) if not results: print('No interaction data found in FDA labels.') sys.exit() for r in results[:2]: brand=r.get('openfda',{}).get('brand_name',['Unknown'])[0] generic=r.get('openfda',{}).get('generic_name',['Unknown'])[0] interactions=r.get('drug_interactions',['N/A'])[0] print(f'--- {brand} ({generic}) ---') print(interactions[:800]) print() " DRUG= " $1 " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $DRUG " ) curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:\" ${ENCODED} \"&count=patient.reaction.reactionmeddrapt.exact&limit=10" \ | python -c " import json,sys data=json.load(sys.stdin) results=data.get('results',[]) if not results: print('No adverse event data found.') sys.exit() print(f'Top adverse events reported:') for r in results[:10]: print(f\" {r['count']:>5}x {r['term']}\") " 4 — PubChem Compound Search COMPOUND= " $1 " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $COMPOUND " ) CID=$(curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/ ${ENCODED} /cids/TXT" | head -1 | tr -d '[:space:]' ) echo "PubChem CID: $CID " curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/ ${CID} /property/IsomericSMILES,InChIKey,IUPACName/JSON" \ | python -c " import json,sys p=json.load(sys.stdin)['PropertyTable']['Properties'][0] print(f\"IUPAC Name : {p.get('IUPACName','N/A')}\") print(f\"SMILES : {p.get('IsomericSMILES','N/A')}\") print(f\"InChIKey : {p.get('InChIKey','N/A')}\") " 5 — Target & Disease Literature (OpenTargets) GENE= " $1 " curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \ -H "Content-Type: application/json" \ -d "{\"query\":\"{ search(queryString: \\\" ${GENE} \\\", entityNames: [\\\"target\\\"], page: {index: 0, size: 1}) { hits { id score object { ... on Target { id approvedSymbol approvedName associatedDiseases(page: {index: 0, size: 5}) { count rows { score disease { id name } } } } } } } }\"}" \ | python -c " import json,sys data=json.load(sys.stdin) hits=data.get('data',{}).get('search',{}).get('hits',[]) if not hits: print('Target not found.') sys.exit() obj=hits[0]['object'] print(f\"Target: {obj.get('approvedSymbol')} — {obj.get('approvedName')}\") assoc=obj.get('associatedDiseases',{}) print(f\"Associated with {assoc.get('count',0)} diseases. Top associations:\") for row in assoc.get('rows',[]): print(f\" Score {row['score']:.3f} | {row['disease']['name']}\") " Reasoning Guidelines When analysing drug-likeness or molecular properties, always: State raw values first — MW, LogP, HBD, HBA, TPSA, RotBonds Apply rule sets — Ro5 (Lipinski), Veber, Ghose filter where relevant Flag liabilities — metabolic hotspots, hERG risk, high TPSA for CNS penetration Suggest optimizations — bioisosteric replacements, prodrug strategies, ring truncation Cite the source API — ChEMBL, PubChem, OpenFDA, or OpenTargets For ADMET questions, reason through Absorption, Distribution, Metabolism, Excretion, Toxicity systematically. See references/ADMET_REFERENCE.md for detailed guidance. Important Notes All APIs are free, public, require no authentication ChEMBL rate limits: add sleep 1 between batch requests FDA data reflects reported adverse events, not necessarily causation Always recommend consulting a licensed pharmacist or physician for clinical decisions Quick Reference Task API Endpoint Find target ChEMBL /api/data/target/search?q= Get bioactivity ChEMBL /api/data/activity?target_chembl_id= Molecule properties PubChem /rest/pug/compound/name/{name}/property/ Drug interactions OpenFDA /drug/label.json?search=drug_interactions: Adverse events OpenFDA /drug/event.json?search=...&count=reaction Gene-disease OpenTargets GraphQL POST /api/v4/graphql