Drug Discovery

药物发现工作流的制药研究助手。在 ChEMBL 上搜索生物活性化合物，计算类药性（Lipinski Ro5、QED、TPSA、合成可及性），通过 OpenFDA 查询药物相互作用，解读 ADMET 特征，并协助先导化合物优化。适用于药物化学问题、分子性质分析、临床药理学及开放科学药物研究。

Skill 元数据


来源	可选 — 通过 `aigenlabs skills install official/research/drug-discovery` 安装
路径	`optional-skills/research/drug-discovery`
版本	`1.0.0`
作者	bennytimz
许可证	MIT
平台	linux, macos, windows
标签	`science`, `chemistry`, `pharmacology`, `research`, `health`

参考：完整 SKILL.md

信息

以下是 AigenLabs 在触发该 skill 时加载的完整 skill 定义。这是 skill 激活时 agent 所看到的指令内容。

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="$1"
ENCODED=$(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()
"

# 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')} 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.

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)

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']}\")
"

4 — PubChem Compound Search

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)

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']}\")
"

推理指南

在分析类药性或分子性质时，始终遵循以下步骤：

先列出原始数值 — MW、LogP、HBD、HBA、TPSA、可旋转键数
应用规则集 — Ro5（Lipinski）、Veber、Ghose 过滤器（视情况而定）
标记风险点 — 代谢热点、hERG 风险、CNS 穿透的高 TPSA
提出优化建议 — 生物等排体替换、前药策略、环截断
注明数据来源 API — ChEMBL、PubChem、OpenFDA 或 OpenTargets

对于 ADMET（吸收、分布、代谢、排泄、毒性）问题，需系统性地逐项推理。详细指导请参阅 references/ADMET_REFERENCE.md。

重要说明

所有 API 均免费、公开，无需身份验证
ChEMBL 速率限制：批量请求之间请添加 sleep 1
FDA 数据反映已报告的不良事件，不一定代表因果关系
临床决策请务必咨询持牌药剂师或医生

快速参考

任务	API	端点
查找靶点	ChEMBL	`/api/data/target/search?q=`
获取生物活性数据	ChEMBL	`/api/data/activity?target_chembl_id=`
分子性质	PubChem	`/rest/pug/compound/name/{name}/property/`
药物相互作用	OpenFDA	`/drug/label.json?search=drug_interactions:`
不良事件	OpenFDA	`/drug/event.json?search=...&count=reaction`
基因-疾病关联	OpenTargets	GraphQL POST `/api/v4/graphql`

Skill 元数据​

参考：完整 SKILL.md​

Drug Discovery & Pharmaceutical Research

Core Workflows​

1 — Bioactive Compound Search (ChEMBL)​

2 — Drug-Likeness Calculation (Lipinski Ro5 + Veber)​

3 — Drug Interaction & Safety Lookup (OpenFDA)​

4 — PubChem Compound Search​

5 — Target & Disease Literature (OpenTargets)​

推理指南​

重要说明​

快速参考​