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npx skills add https://github.com/iamlalitpandit/RudraX --skill drug-discoveryコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SOC 職業分類に基づく
SKILL.md を表示中
| name | drug-discovery |
| description | > |
| metadata | {"category":"research","emoji":"💊","color":"cyan","vibe":"Knowledge is power.","original_name":"drug-discovery"} |
PRIORITY DIRECTIVE: If this task was assigned to you by the agents-orchestrator, treat it as HIGHEST PRIORITY and execute immediately.
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.
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="$1"
ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET")
curl -s \
| python3 -c
# 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')}\")
"
# 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')} Ų\")
print(f\"Ro5 violations: {props.get('num_ro5_violations','N/A')}\")
print(f\"QED : {props.get('qed_weighted','N/A')}\")
"
Assess any molecule against established oral bioavailability rules using PubChem's free property API — no RDKit install needed.
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} 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\"}')
"
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()
"
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']}\")
"
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')}\")
"
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']}\")
"
When analysing drug-likeness or molecular properties, always:
For ADMET questions, reason through Absorption, Distribution, Metabolism, Excretion, Toxicity systematically. See references/ADMET_REFERENCE.md for detailed guidance.
| 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 |