Skill v1.0.1
currentAutomated scan96/100test(hermes_state): trim compacted-paging tests to the invariants that fail on base
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,sysdata=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 IDTARGET_ID="$1" # e.g. CHEMBL203curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \| python -c "import json,sysdata=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 IDMOL_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,sysm=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,sysdata=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,sysdata=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,sysdata=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,sysp=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,sysdata=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 |