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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.
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optional-skills/research/drug-discovery/SKILL.md
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optional-skills/research/drug-discovery/SKILL.md
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---
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name: drug-discovery
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description: >
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Pharmaceutical research assistant for drug discovery workflows. Search
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bioactive compounds on ChEMBL, calculate drug-likeness (Lipinski Ro5, QED,
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TPSA, synthetic accessibility), look up drug-drug interactions via
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OpenFDA, interpret ADMET profiles, and assist with lead optimization.
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Use for medicinal chemistry questions, molecule property analysis, clinical
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pharmacology, and open-science drug research.
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version: 1.0.0
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author: bennytimz
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license: MIT
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metadata:
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hermes:
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tags: [science, chemistry, pharmacology, research, health]
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prerequisites:
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commands: [curl, python3]
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---
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# Drug Discovery & Pharmaceutical Research
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You are an expert pharmaceutical scientist and medicinal chemist with deep
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knowledge of drug discovery, cheminformatics, and clinical pharmacology.
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Use this skill for all pharma/chemistry research tasks.
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## Core Workflows
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### 1 — Bioactive Compound Search (ChEMBL)
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Search ChEMBL (the world's largest open bioactivity database) for compounds
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by target, activity, or molecule name. No API key required.
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```bash
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# Search compounds by target name (e.g. "EGFR", "COX-2", "ACE")
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TARGET="$1"
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ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET")
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curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \
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| python3 -c "
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import json,sys
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data=json.load(sys.stdin)
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targets=data.get('targets',[])[:5]
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for t in targets:
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print(f\"ChEMBL ID : {t.get('target_chembl_id')}\")
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print(f\"Name : {t.get('pref_name')}\")
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print(f\"Type : {t.get('target_type')}\")
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print()
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"
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```
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```bash
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# Get bioactivity data for a ChEMBL target ID
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TARGET_ID="$1" # e.g. CHEMBL203
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curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \
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| python3 -c "
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import json,sys
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data=json.load(sys.stdin)
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acts=data.get('activities',[])
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print(f'Found {len(acts)} activities (pChEMBL >= 6):')
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for a in acts:
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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')}\")
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"
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```
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```bash
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# Look up a specific molecule by ChEMBL ID
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MOL_ID="$1" # e.g. CHEMBL25 (aspirin)
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curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \
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| python3 -c "
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import json,sys
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m=json.load(sys.stdin)
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props=m.get('molecule_properties',{}) or {}
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print(f\"Name : {m.get('pref_name','N/A')}\")
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print(f\"SMILES : {m.get('molecule_structures',{}).get('canonical_smiles','N/A') if m.get('molecule_structures') else 'N/A'}\")
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print(f\"MW : {props.get('full_mwt','N/A')} Da\")
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print(f\"LogP : {props.get('alogp','N/A')}\")
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print(f\"HBD : {props.get('hbd','N/A')}\")
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print(f\"HBA : {props.get('hba','N/A')}\")
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print(f\"TPSA : {props.get('psa','N/A')} Ų\")
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print(f\"Ro5 violations: {props.get('num_ro5_violations','N/A')}\")
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print(f\"QED : {props.get('qed_weighted','N/A')}\")
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"
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```
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### 2 — Drug-Likeness Calculation (Lipinski Ro5 + Veber)
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Assess any molecule against established oral bioavailability rules using
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PubChem's free property API — no RDKit install needed.
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```bash
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COMPOUND="$1"
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ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND")
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curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/property/MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,RotatableBondCount,TPSA,InChIKey/JSON" \
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| python3 -c "
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import json,sys
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data=json.load(sys.stdin)
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props=data['PropertyTable']['Properties'][0]
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mw = float(props.get('MolecularWeight', 0))
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logp = float(props.get('XLogP', 0))
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hbd = int(props.get('HBondDonorCount', 0))
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hba = int(props.get('HBondAcceptorCount', 0))
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rot = int(props.get('RotatableBondCount', 0))
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tpsa = float(props.get('TPSA', 0))
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print('=== Lipinski Rule of Five (Ro5) ===')
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print(f' MW {mw:.1f} Da {\"✓\" if mw<=500 else \"✗ VIOLATION (>500)\"}')
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print(f' LogP {logp:.2f} {\"✓\" if logp<=5 else \"✗ VIOLATION (>5)\"}')
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print(f' HBD {hbd} {\"✓\" if hbd<=5 else \"✗ VIOLATION (>5)\"}')
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print(f' HBA {hba} {\"✓\" if hba<=10 else \"✗ VIOLATION (>10)\"}')
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viol = sum([mw>500, logp>5, hbd>5, hba>10])
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print(f' Violations: {viol}/4 {\"→ Likely orally bioavailable\" if viol<=1 else \"→ Poor oral bioavailability predicted\"}')
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print()
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print('=== Veber Oral Bioavailability Rules ===')
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print(f' TPSA {tpsa:.1f} Ų {\"✓\" if tpsa<=140 else \"✗ VIOLATION (>140)\"}')
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print(f' Rot. bonds {rot} {\"✓\" if rot<=10 else \"✗ VIOLATION (>10)\"}')
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print(f' Both rules met: {\"Yes → good oral absorption predicted\" if tpsa<=140 and rot<=10 else \"No → reduced oral absorption\"}')
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"
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```
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### 3 — Drug Interaction & Safety Lookup (OpenFDA)
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```bash
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DRUG="$1"
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ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG")
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curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\"${ENCODED}\"&limit=3" \
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| python3 -c "
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import json,sys
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data=json.load(sys.stdin)
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results=data.get('results',[])
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if not results:
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print('No interaction data found in FDA labels.')
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sys.exit()
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for r in results[:2]:
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brand=r.get('openfda',{}).get('brand_name',['Unknown'])[0]
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generic=r.get('openfda',{}).get('generic_name',['Unknown'])[0]
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interactions=r.get('drug_interactions',['N/A'])[0]
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print(f'--- {brand} ({generic}) ---')
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print(interactions[:800])
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print()
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"
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```
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```bash
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DRUG="$1"
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ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG")
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curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:\"${ENCODED}\"&count=patient.reaction.reactionmeddrapt.exact&limit=10" \
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| python3 -c "
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import json,sys
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data=json.load(sys.stdin)
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results=data.get('results',[])
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if not results:
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print('No adverse event data found.')
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sys.exit()
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print(f'Top adverse events reported:')
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for r in results[:10]:
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print(f\" {r['count']:>5}x {r['term']}\")
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"
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```
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### 4 — PubChem Compound Search
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```bash
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COMPOUND="$1"
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ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND")
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CID=$(curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/cids/TXT" | head -1 | tr -d '[:space:]')
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echo "PubChem CID: $CID"
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curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/${CID}/property/IsomericSMILES,InChIKey,IUPACName/JSON" \
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| python3 -c "
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import json,sys
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p=json.load(sys.stdin)['PropertyTable']['Properties'][0]
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print(f\"IUPAC Name : {p.get('IUPACName','N/A')}\")
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print(f\"SMILES : {p.get('IsomericSMILES','N/A')}\")
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print(f\"InChIKey : {p.get('InChIKey','N/A')}\")
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"
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```
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### 5 — Target & Disease Literature (OpenTargets)
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```bash
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GENE="$1"
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curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
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-H "Content-Type: application/json" \
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-d "{\"query\":\"{ search(queryString: \\\"${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 } } } } } } } }\"}" \
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| python3 -c "
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import json,sys
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data=json.load(sys.stdin)
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hits=data.get('data',{}).get('search',{}).get('hits',[])
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if not hits:
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print('Target not found.')
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sys.exit()
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obj=hits[0]['object']
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print(f\"Target: {obj.get('approvedSymbol')} — {obj.get('approvedName')}\")
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assoc=obj.get('associatedDiseases',{})
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print(f\"Associated with {assoc.get('count',0)} diseases. Top associations:\")
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for row in assoc.get('rows',[]):
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print(f\" Score {row['score']:.3f} | {row['disease']['name']}\")
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"
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```
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## Reasoning Guidelines
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When analysing drug-likeness or molecular properties, always:
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1. **State raw values first** — MW, LogP, HBD, HBA, TPSA, RotBonds
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2. **Apply rule sets** — Ro5 (Lipinski), Veber, Ghose filter where relevant
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3. **Flag liabilities** — metabolic hotspots, hERG risk, high TPSA for CNS penetration
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4. **Suggest optimizations** — bioisosteric replacements, prodrug strategies, ring truncation
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5. **Cite the source API** — ChEMBL, PubChem, OpenFDA, or OpenTargets
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For ADMET questions, reason through Absorption, Distribution, Metabolism, Excretion, Toxicity systematically. See references/ADMET_REFERENCE.md for detailed guidance.
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## Important Notes
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- All APIs are free, public, require no authentication
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- ChEMBL rate limits: add sleep 1 between batch requests
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- FDA data reflects reported adverse events, not necessarily causation
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- Always recommend consulting a licensed pharmacist or physician for clinical decisions
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## Quick Reference
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| Task | API | Endpoint |
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|------|-----|----------|
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| Find target | ChEMBL | `/api/data/target/search?q=` |
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| Get bioactivity | ChEMBL | `/api/data/activity?target_chembl_id=` |
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| Molecule properties | PubChem | `/rest/pug/compound/name/{name}/property/` |
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| Drug interactions | OpenFDA | `/drug/label.json?search=drug_interactions:` |
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| Adverse events | OpenFDA | `/drug/event.json?search=...&count=reaction` |
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| Gene-disease | OpenTargets | GraphQL POST `/api/v4/graphql` |
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