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nousresearch/hermes-agent

drug-discovery

Drug discovery: ChEMBL search, drug-likeness, interactions.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/nousresearch/hermes-agent --skill drug-discovery

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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="$1"
ENCODED=$(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()
"
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" \
  | 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')}\")
"
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" \
  | 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.

bash
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)

bash
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()
"
bash
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

bash
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)

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 } } } } } } } }\"}" \
  | 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:

  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

TaskAPIEndpoint
Find targetChEMBL/api/data/target/search?q=
Get bioactivityChEMBL/api/data/activity?target_chembl_id=
Molecule propertiesPubChem/rest/pug/compound/name/{name}/property/
Drug interactionsOpenFDA/drug/label.json?search=drug_interactions:
Adverse eventsOpenFDA/drug/event.json?search=...&count=reaction
Gene-diseaseOpenTargetsGraphQL POST /api/v4/graphql
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