feat(skills): add drug-discovery optional skill — ChEMBL, PubChem, OpenFDA, ADMET analysis

Pharmaceutical research skill covering bioactive compound search (ChEMBL), drug-likeness screening (Lipinski Ro5 + Veber via PubChem), drug-drug interaction lookups (OpenFDA), gene-disease associations (OpenTargets GraphQL), and ADMET reasoning guidance. All free public APIs, zero auth, stdlib-only Python. Includes helper scripts for batch Ro5 screening and target-to-compound pipelines. Moved to optional-skills/research/ (niche domain skill, not built-in). Fixed: authors→author frontmatter, removed unused jq prerequisite, bare except→except Exception. Co-authored-by: bennytimz <oluwadareab12@gmail.com> Salvaged from PR #8695.
2026-04-25 00:51:20 +00:00 · 2026-04-13 22:58:51 -07:00 · 2026-04-13 22:58:51 -07:00 · a91b9bb855
commit a91b9bb855
parent d631431872
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--- a/optional-skills/research/drug-discovery/SKILL.md
+++ b/optional-skills/research/drug-discovery/SKILL.md
@ -0,0 +1,226 @@
+---
+name: drug-discovery
+description: >
+  Pharmaceutical research assistant for drug discovery workflows. Search
+  bioactive compounds on ChEMBL, calculate drug-likeness (Lipinski Ro5, QED,
+  TPSA, synthetic accessibility), look up drug-drug interactions via
+  OpenFDA, interpret ADMET profiles, and assist with lead optimization.
+  Use for medicinal chemistry questions, molecule property analysis, clinical
+  pharmacology, and open-science drug research.
+version: 1.0.0
+author: bennytimz
+license: MIT
+metadata:
+  hermes:
+    tags: [science, chemistry, pharmacology, research, health]
+prerequisites:
+  commands: [curl, python3]
+---
+
+# 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.
+
+```bash
+# Search compounds by target name (e.g. "EGFR", "COX-2", "ACE")
+TARGET="$1"
+ENCODED=$(python3 -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" \
+  | python3 -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()
+"
+```
+
+```bash
+# 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" \
+  | python3 -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')}\")
+"
+```
+
+```bash
+# 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" \
+  | python3 -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')} Å²\")
+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.
+
+```bash
+COMPOUND="$1"
+ENCODED=$(python3 -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" \
+  | python3 -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} Å²   {\"✓\" 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)
+
+```bash
+DRUG="$1"
+ENCODED=$(python3 -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" \
+  | python3 -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()
+"
+```
+
+```bash
+DRUG="$1"
+ENCODED=$(python3 -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" \
+  | python3 -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
+
+```bash
+COMPOUND="$1"
+ENCODED=$(python3 -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" \
+  | python3 -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)
+
+```bash
+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 } } } } } } } }\"}" \
+  | python3 -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:
+
+1. **State raw values first** — MW, LogP, HBD, HBA, TPSA, RotBonds
+2. **Apply rule sets** — Ro5 (Lipinski), Veber, Ghose filter where relevant
+3. **Flag liabilities** — metabolic hotspots, hERG risk, high TPSA for CNS penetration
+4. **Suggest optimizations** — bioisosteric replacements, prodrug strategies, ring truncation
+5. **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` |
--- a/optional-skills/research/drug-discovery/references/ADMET_REFERENCE.md
+++ b/optional-skills/research/drug-discovery/references/ADMET_REFERENCE.md
@ -0,0 +1,66 @@
+# ADMET Reference Guide
+
+Comprehensive reference for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis in drug discovery.
+
+## Drug-Likeness Rule Sets
+
+### Lipinski's Rule of Five (Ro5)
+
+| Property | Threshold |
+|----------|-----------|
+| Molecular Weight (MW) | ≤ 500 Da |
+| Lipophilicity (LogP) | ≤ 5 |
+| H-Bond Donors (HBD) | ≤ 5 |
+| H-Bond Acceptors (HBA) | ≤ 10 |
+
+Reference: Lipinski et al., Adv. Drug Deliv. Rev. 23, 3–25 (1997).
+
+### Veber's Oral Bioavailability Rules
+
+| Property | Threshold |
+|----------|-----------|
+| TPSA | ≤ 140 Å² |
+| Rotatable Bonds | ≤ 10 |
+
+Reference: Veber et al., J. Med. Chem. 45, 2615–2623 (2002).
+
+### CNS Penetration (BBB)
+
+| Property | CNS-Optimal |
+|----------|-------------|
+| MW | ≤ 400 Da |
+| LogP | 1–3 |
+| TPSA | < 90 Å² |
+| HBD | ≤ 3 |
+
+## CYP450 Metabolism
+
+| Isoform | % Drugs | Notable inhibitors |
+|---------|---------|-------------------|
+| CYP3A4 | ~50% | Grapefruit, ketoconazole |
+| CYP2D6 | ~25% | Fluoxetine, paroxetine |
+| CYP2C9 | ~15% | Fluconazole, amiodarone |
+| CYP2C19 | ~10% | Omeprazole, fluoxetine |
+| CYP1A2 | ~5% | Fluvoxamine, ciprofloxacin |
+
+## hERG Cardiac Toxicity Risk
+
+Structural alerts: basic nitrogen (pKa 7–9) + aromatic ring + hydrophobic moiety, LogP > 3.5 + basic amine.
+
+Mitigation: reduce basicity, introduce polar groups, break planarity.
+
+## Common Bioisosteric Replacements
+
+| Original | Bioisostere | Purpose |
+|----------|-------------|---------|
+| -COOH | -tetrazole, -SO₂NH₂ | Improve permeability |
+| -OH (phenol) | -F, -CN | Reduce glucuronidation |
+| Phenyl | Pyridine, thiophene | Reduce LogP |
+| Ester | -CONHR | Reduce hydrolysis |
+
+## Key APIs
+
+- ChEMBL: https://www.ebi.ac.uk/chembl/api/data/
+- PubChem: https://pubchem.ncbi.nlm.nih.gov/rest/pug/
+- OpenFDA: https://api.fda.gov/drug/
+- OpenTargets GraphQL: https://api.platform.opentargets.org/api/v4/graphql
--- a/optional-skills/research/drug-discovery/scripts/chembl_target.py
+++ b/optional-skills/research/drug-discovery/scripts/chembl_target.py
@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""
+chembl_target.py — Search ChEMBL for a target and retrieve top active compounds.
+Usage: python3 chembl_target.py "EGFR" --min-pchembl 7 --limit 20
+No external dependencies.
+"""
+import sys, json, time, argparse
+import urllib.request, urllib.parse, urllib.error
+
+BASE = "https://www.ebi.ac.uk/chembl/api/data"
+
+def get(endpoint):
+    try:
+        req = urllib.request.Request(f"{BASE}{endpoint}", headers={"Accept":"application/json"})
+        with urllib.request.urlopen(req, timeout=15) as r:
+            return json.loads(r.read())
+    except Exception as e:
+        print(f"API error: {e}", file=sys.stderr); return None
+
+def main():
+    parser = argparse.ArgumentParser(description="ChEMBL target → active compounds")
+    parser.add_argument("target")
+    parser.add_argument("--min-pchembl", type=float, default=6.0)
+    parser.add_argument("--limit", type=int, default=10)
+    args = parser.parse_args()
+
+    enc = urllib.parse.quote(args.target)
+    data = get(f"/target/search?q={enc}&limit=5&format=json")
+    if not data or not data.get("targets"):
+        print("No targets found."); sys.exit(1)
+
+    t = data["targets"][0]
+    tid = t.get("target_chembl_id","")
+    print(f"\nTarget: {t.get('pref_name')} ({tid})")
+    print(f"Type: {t.get('target_type')} | Organism: {t.get('organism','N/A')}")
+    print(f"\nFetching compounds with pChEMBL ≥ {args.min_pchembl}...\n")
+
+    acts = get(f"/activity?target_chembl_id={tid}&pchembl_value__gte={args.min_pchembl}&assay_type=B&limit={args.limit}&order_by=-pchembl_value&format=json")
+    if not acts or not acts.get("activities"):
+        print("No activities found."); sys.exit(0)
+
+    print(f"{'Molecule':<18} {'pChEMBL':>8} {'Type':<12} {'Value':<10} {'Units'}")
+    print("-"*65)
+    seen = set()
+    for a in acts["activities"]:
+        mid = a.get("molecule_chembl_id","N/A")
+        if mid in seen: continue
+        seen.add(mid)
+        print(f"{mid:<18} {str(a.get('pchembl_value','N/A')):>8} {str(a.get('standard_type','N/A')):<12} {str(a.get('standard_value','N/A')):<10} {a.get('standard_units','N/A')}")
+        time.sleep(0.1)
+    print(f"\nTotal: {len(seen)} unique molecules")
+
+if __name__ == "__main__": main()
--- a/optional-skills/research/drug-discovery/scripts/ro5_screen.py
+++ b/optional-skills/research/drug-discovery/scripts/ro5_screen.py
@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+"""
+ro5_screen.py — Batch Lipinski Ro5 + Veber screening via PubChem API.
+Usage: python3 ro5_screen.py aspirin ibuprofen paracetamol
+No external dependencies beyond stdlib.
+"""
+import sys, json, time, argparse
+import urllib.request, urllib.parse, urllib.error
+
+BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name"
+PROPS = "MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,RotatableBondCount,TPSA"
+
+def fetch(name):
+    url = f"{BASE}/{urllib.parse.quote(name)}/property/{PROPS}/JSON"
+    try:
+        with urllib.request.urlopen(url, timeout=10) as r:
+            return json.loads(r.read())["PropertyTable"]["Properties"][0]
+    except Exception:
+        return None
+
+def check(p):
+    mw,logp,hbd,hba,rot,tpsa = float(p.get("MolecularWeight",0)),float(p.get("XLogP",0)),int(p.get("HBondDonorCount",0)),int(p.get("HBondAcceptorCount",0)),int(p.get("RotatableBondCount",0)),float(p.get("TPSA",0))
+    v = sum([mw>500,logp>5,hbd>5,hba>10])
+    return dict(mw=mw,logp=logp,hbd=hbd,hba=hba,rot=rot,tpsa=tpsa,violations=v,ro5=v<=1,veber=tpsa<=140 and rot<=10,ok=v<=1 and tpsa<=140 and rot<=10)
+
+def report(name, r):
+    if not r: print(f"✗ {name:30s} — not found"); return
+    s = "✓ PASS" if r["ok"] else "✗ FAIL"
+    flags = (f" [Ro5 violations:{r['violations']}]" if not r["ro5"] else "") + (" [Veber fail]" if not r["veber"] else "")
+    print(f"{s}  {name:28s} MW={r['mw']:.0f} LogP={r['logp']:.2f} HBD={r['hbd']} HBA={r['hba']} TPSA={r['tpsa']:.0f} RotB={r['rot']}{flags}")
+
+def main():
+    compounds = sys.stdin.read().splitlines() if len(sys.argv)<2 or sys.argv[1]=="-" else sys.argv[1:]
+    print(f"\n{'Status':<8} {'Compound':<30} Properties\n" + "-"*85)
+    passed = 0
+    for name in compounds:
+        props = fetch(name.strip())
+        result = check(props) if props else None
+        report(name.strip(), result)
+        if result and result["ok"]: passed += 1
+        time.sleep(0.3)
+    print(f"\nSummary: {passed}/{len(compounds)} passed Ro5 + Veber.\n")
+
+if __name__ == "__main__": main()