| name | scientific-drugbank-resources |
| description | DrugBank リソーススキル。DrugBank API を用いた薬剤記述・
薬理情報・標的タンパク質・薬物相互作用検索。
ToolUniverse 連携: drugbank。
|
| tu_tools | [{"key":"drugbank","name":"DrugBank","description":"薬剤データベース API"}] |
Scientific DrugBank Resources
DrugBank API を活用した薬剤記述・薬理情報 (MOA)・標的タンパク質
検索・薬物相互作用 (DDI) パイプラインを提供する。
When to Use
- 薬剤の基本情報 (名前・分類・構造) を検索するとき
- 薬理メカニズム (MOA) を調べるとき
- 標的タンパク質から薬剤を逆引き検索するとき
- 薬物相互作用 (DDI) を確認するとき
- 薬剤の ADMET プロパティを取得するとき
Quick Start
1. 薬剤検索・基本情報
import requests
import pandas as pd
DRUGBANK_API = "https://api.drugbank.com/v1"
def drugbank_search(query, limit=25, api_key=None):
"""
DrugBank — 薬剤テキスト検索。
Parameters:
query: str — 検索クエリ (例: "imatinib")
limit: int — 最大結果数
api_key: str — DrugBank API キー
"""
headers = {}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
url = f"{DRUGBANK_API}/drugs"
params = {"q": query, "per_page": limit}
resp = requests.get(url, params=params,
headers=headers, timeout=30)
resp.raise_for_status()
data = resp.json()
rows = []
for d in data:
rows.append({
"drugbank_id": d.get("drugbank_id", ""),
"name": d.get("name", ""),
"cas_number": d.get("cas_number", ""),
"drug_type": d.get("type", ""),
"state": d.get("state", ""),
"description": (d.get("description", "")
[:200]),
})
df = pd.DataFrame(rows)
print(f"DrugBank search: '{query}' → {len(df)} drugs")
return df
def drugbank_drug_detail(drugbank_id, api_key=None):
"""
DrugBank — 薬剤詳細取得。
Parameters:
drugbank_id: str — DrugBank ID (例: "DB01254")
api_key: str — DrugBank API キー
"""
headers = {}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
url = f"{DRUGBANK_API}/drugs/{drugbank_id}"
resp = requests.get(url, headers=headers, timeout=30)
resp.raise_for_status()
data = resp.json()
result = {
"drugbank_id": data.get("drugbank_id", ""),
"name": data.get("name", ""),
"description": data.get("description", ""),
"indication": data.get("indication", ""),
"pharmacodynamics": data.get(
"pharmacodynamics", ""),
"mechanism_of_action": data.get(
"mechanism_of_action", ""),
"absorption": data.get("absorption", ""),
"half_life": data.get("half_life", ""),
"protein_binding": data.get(
"protein_binding", ""),
"molecular_weight": data.get(
"average_molecular_weight", ""),
}
return result
2. 標的タンパク質検索
def drugbank_targets(drugbank_id, api_key=None):
"""
DrugBank — 薬剤の標的タンパク質取得。
Parameters:
drugbank_id: str — DrugBank ID
api_key: str — DrugBank API キー
"""
headers = {}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
url = f"{DRUGBANK_API}/drugs/{drugbank_id}/targets"
resp = requests.get(url, headers=headers, timeout=30)
resp.raise_for_status()
data = resp.json()
rows = []
for t in data:
polypeptide = t.get("polypeptide", {}) or {}
rows.append({
"drugbank_id": drugbank_id,
"target_name": t.get("name", ""),
"organism": t.get("organism", ""),
"known_action": t.get("known_action", ""),
"gene_name": polypeptide.get(
"gene_name", ""),
"uniprot_id": polypeptide.get(
"external_identifiers", {}).get(
"UniProtKB", ""),
})
df = pd.DataFrame(rows)
print(f"DrugBank targets: {drugbank_id} "
f"→ {len(df)} targets")
return df
3. 薬物相互作用 (DDI)
def drugbank_interactions(drugbank_id, api_key=None):
"""
DrugBank — 薬物相互作用取得。
Parameters:
drugbank_id: str — DrugBank ID
api_key: str — DrugBank API キー
"""
headers = {}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
url = (f"{DRUGBANK_API}/drugs/"
f"{drugbank_id}/drug_interactions")
resp = requests.get(url, headers=headers, timeout=30)
resp.raise_for_status()
data = resp.json()
rows = []
for inter in data:
rows.append({
"drug_a": drugbank_id,
"drug_b_id": inter.get(
"drugbank_id", ""),
"drug_b_name": inter.get("name", ""),
"description": inter.get(
"description", "")[:300],
})
df = pd.DataFrame(rows)
print(f"DrugBank DDI: {drugbank_id} "
f"→ {len(df)} interactions")
return df
4. DrugBank 統合パイプライン
def drugbank_pipeline(drug_name, api_key=None,
output_dir="results"):
"""
DrugBank 統合パイプライン。
Parameters:
drug_name: str — 薬剤名 (例: "imatinib")
api_key: str — DrugBank API キー
output_dir: str — 出力ディレクトリ
"""
from pathlib import Path
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
results = drugbank_search(drug_name,
api_key=api_key)
results.to_csv(output_dir / "drugbank_search.csv",
index=False)
if results.empty:
print(f"DrugBank: '{drug_name}' not found")
return {"search": results}
db_id = results.iloc[0]["drugbank_id"]
detail = drugbank_drug_detail(db_id,
api_key=api_key)
pd.DataFrame([detail]).to_csv(
output_dir / "drugbank_detail.csv",
index=False)
targets = drugbank_targets(db_id,
api_key=api_key)
targets.to_csv(output_dir / "drugbank_targets.csv",
index=False)
ddi = drugbank_interactions(db_id,
api_key=api_key)
ddi.to_csv(output_dir / "drugbank_ddi.csv",
index=False)
print(f"DrugBank pipeline: {drug_name} → {output_dir}")
return {"detail": detail, "targets": targets,
: ddi}
ToolUniverse 連携
| TU Key | ツール名 | 連携内容 |
|---|
drugbank | DrugBank | 薬剤データベース API |
パイプライン統合
drug-target-profiling → drugbank-resources → admet-pharmacokinetics
(標的プロファイリング) (DrugBank API) (ADMET 予測)
│ │ ↓
opentargets-genetics ──────────┘ compound-screening
(OT 薬剤エビデンス) (ZINC 化合物検索)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|
results/drugbank_search.csv | 薬剤検索結果 | → drug-target-profiling |
results/drugbank_detail.csv | 薬剤詳細 | → admet-pharmacokinetics |
results/drugbank_targets.csv | 標的タンパク質 | → protein-interaction-network |
results/drugbank_ddi.csv | 薬物相互作用 | → pharmacogenomics |
