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ADMET Prediction & Drug Candidate Profiling

ADMET预测与药物候选物分析

ADMET reasoning: a drug fails if it can't be absorbed, distributes to wrong tissues, isn't metabolized safely, or isn't excreted. Evaluate each property independently — good absorption doesn't compensate for liver toxicity. The ADME properties determine whether a compound reaches its target at therapeutic concentrations; toxicity determines whether it's safe to do so. Prioritize experimental data (T2) over computational predictions (T3) — ADMETAI predictions are screening tools, not definitive verdicts. When a FAIL is flagged in any toxicity category (hERG, AMES, DILI), treat it as program-limiting until wet-lab data refutes it.
LOOK UP DON'T GUESS: never assume SMILES, CID, or experimental LD50 values — always call PubChem to resolve compound identity before any ADMETAI or PubChemTox call.
Comprehensive pharmacokinetic and toxicity profiling integrating AI-based ADMET predictions, rule-based drug-likeness filters, and experimental benchmarks from curated databases.
ADMET分析逻辑：若药物无法被吸收、分布至错误组织、代谢不安全或无法排泄，就会研发失败。需独立评估每个属性——良好的吸收无法弥补肝毒性问题。ADME属性决定化合物能否以治疗浓度抵达靶点；毒性则决定其是否安全。优先采用实验数据（T2）而非计算预测数据（T3）——ADMETAI预测仅为筛选工具，并非最终结论。当任何毒性类别（hERG、AMES、DILI）标记为FAIL时，除非湿实验数据推翻该结果，否则将其视为项目限制因素。
查资料而非猜测：切勿假设SMILES、CID或实验LD50值——在调用ADMETAI或PubChemTox之前，务必调用PubChem确认化合物身份。
整合基于AI的ADMET预测、基于规则的类药性筛选以及来自 curated 数据库的实验基准数据，进行全面的药代动力学与毒性分析。

When to Use This Skill

何时使用该技能

Triggers:
  • "What are the ADMET properties of [compound]?"
  • "Is [drug] likely to cross the blood-brain barrier?"
  • "Predict the toxicity of this SMILES: ..."
  • "Does [compound] violate Lipinski's rule of five?"
  • "Assess the drug-likeness of [molecule]"
  • "What are the CYP interactions for [drug]?"
  • "Pharmacokinetic profile of [compound]"
  • "Is [compound] orally bioavailable?"
  • "What is the LD50 / hERG liability of [molecule]?"
Input: Drug name (e.g., "ibuprofen") OR SMILES string (e.g., "CC(C)Cc1ccc(cc1)C(C)C(=O)O")
触发场景:
  • "[化合物]的ADMET属性是什么？"
  • "[药物]能否穿透血脑屏障？"
  • "预测该SMILES的毒性：..."
  • "[化合物]是否违反Lipinski五规则？"
  • "评估[分子]的类药性"
  • "[药物]的CYP相互作用有哪些？"
  • "[化合物]的药代动力学分析"
  • "[化合物]是否具有口服生物利用度？"
  • "[分子]的LD50 / hERG风险是什么？"
输入：药物名称（例如："ibuprofen"）或SMILES字符串（例如："CC(C)Cc1ccc(cc1)C(C)C(=O)O"）

COMPUTE, DON'T DESCRIBE

计算而非描述

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
当分析需要计算（统计、数据处理、评分、富集）时，通过Bash编写并运行Python代码。不要描述你会做什么——直接执行并报告实际结果。使用ToolUniverse工具检索数据，然后用Python（pandas、scipy、statsmodels、matplotlib）进行分析。

KEY PRINCIPLES

核心原则

  1. Resolve identity first - Always convert drug name to SMILES before calling ADMETAI tools
  2. ADMETAI tools require 
    tooluniverse[ml]
     - If import fails, skip to SwissADME/PubChemTox fallbacks
  3. All ADMETAI tools take 
    smiles: list[str]
     - Always wrap in a list, even for one compound
  4. SwissADME takes 
    smiles: str
     - Single string, NOT a list (SOAP-style with 
    operation
    param)
  5. PubChemTox tools accept 
    cid
    or 
    compound_name
     - Use CID when available for reliability
  6. Evidence grading mandatory - Predictions (T3), experimental data (T2), regulatory (T1)
  7. Scorecard output - Every analysis must end with a pass/warn/fail scorecard
  8. Explain significance - State WHY each property matters for drug development
  1. 先确认身份 - 在调用ADMETAI工具前，务必将药物名称转换为SMILES
  2. ADMETAI工具需要
    tooluniverse[ml]
     - 若导入失败，切换至SwissADME/PubChemTox备选方案
  3. 所有ADMETAI工具接受
    smiles: list[str]
     - 即使是单个化合物，也要包裹在列表中
  4. SwissADME接受
    smiles: str
     - 单个字符串，而非列表（带
    operation
    参数的SOAP风格）
  5. PubChemTox工具接受
    cid
    或
    compound_name
     - 若有可用CID，优先使用以保证可靠性
  6. 必须进行证据分级 - 预测数据（T3）、实验数据（T2）、监管数据（T1）
  7. 输出评分卡 - 每次分析必须以通过/警告/失败的评分卡结尾
  8. 解释重要性 - 说明每个属性对药物研发的重要性

Evidence Grading

证据分级

TierLabelSource
T1Regulatory/ClinicalFDA labels, ChEMBL max clinical phase
T2ExperimentalPubChemTox LD50/LC50, in vitro AMES, animal studies
T3ComputationalADMETAI predictions, SwissADME calculations
T4AnnotationDatabase cross-references, text-mined
层级标签来源
T1监管/临床FDA标签、ChEMBL最高临床阶段
T2实验PubChemTox LD50/LC50、体外AMES试验、动物研究
T3计算ADMETAI预测、SwissADME计算
T4注释数据库交叉引用、文本挖掘

Workflow: 5-Phase ADMET Profiling

工作流程：五阶段ADMET分析

User Query (drug name or SMILES)
|
+-- PHASE 1: Compound Identity Resolution
|   PubChem name->CID->SMILES, or validate input SMILES
|
+-- PHASE 2: Physicochemical & Drug-Likeness
|   ADMETAI physicochemical + SwissADME druglikeness -> Lipinski/Veber
|
+-- PHASE 3: ADME Predictions
|   BBB, bioavailability, CYP interactions, clearance, solubility
|
+-- PHASE 4: Toxicity Assessment
|   ADMETAI tox + PubChemTox experimental + nuclear receptor + stress
|
+-- PHASE 5: Scorecard & Clinical Context
|   ChEMBL max phase, aggregate pass/warn/fail, final recommendation
用户查询（药物名称或SMILES）
|
+-- 阶段1：化合物身份确认
|   PubChem 名称->CID->SMILES，或验证输入的SMILES
|
+-- 阶段2：物理化学性质与类药性
|   ADMETAI物理化学分析 + SwissADME类药性分析 -> Lipinski/Veber规则验证
|
+-- 阶段3：ADME预测
|   血脑屏障穿透性、生物利用度、CYP相互作用、清除率、溶解度
|
+-- 阶段4：毒性评估
|   ADMETAI毒性预测 + PubChemTox实验数据 + 核受体分析 + 应激反应分析
|
+-- 阶段5：评分卡与临床背景
|   ChEMBL最高阶段、汇总通过/警告/失败结果、最终建议

PHASE 1: Compound Identity Resolution

阶段1：化合物身份确认

Goal: Obtain SMILES, PubChem CID, and basic identifiers for the query compound.
Steps:
  1. If input is a drug name:
    • Call 
      PubChem_get_CID_by_compound_name(name=<drug_name>)
      to get CID
    • Call 
      PubChem_get_compound_properties_by_CID(cid=<CID>)
      to get SMILES and MW
    • Extract 
      ConnectivitySMILES
      from the response (NOT 
      CanonicalSMILES
      )
  2. If input is a SMILES string:
    • Call 
      PubChem_get_CID_by_SMILES(smiles=<SMILES>)
      to get CID
    • Call 
      PubChem_get_compound_properties_by_CID(cid=<CID>)
      for compound name and MW
    • Use the input SMILES for all subsequent ADMETAI calls
  3. Record:
    • Compound name, CID, SMILES, molecular formula, molecular weight, IUPAC name
    • If CID lookup fails, proceed with SMILES only (ADMETAI does not need CID)
Why this matters: ADMETAI tools require SMILES input. PubChemTox tools work best with CID. Resolving both ensures all downstream tools can be called. PubChem is the authoritative source for SMILES canonicalization.
Fallback: If PubChem has no entry, the user must provide SMILES directly. Cannot proceed without SMILES.
目标：获取查询化合物的SMILES、PubChem CID及基础标识符。
步骤:
  1. 若输入为药物名称:
    • 调用
      PubChem_get_CID_by_compound_name(name=<drug_name>)
      获取CID
    • 调用
      PubChem_get_compound_properties_by_CID(cid=<CID>)
      获取SMILES及分子量（MW）
    • 从响应中提取
      ConnectivitySMILES
      （而非
      CanonicalSMILES
      ）
  2. 若输入为SMILES字符串:
    • 调用
      PubChem_get_CID_by_SMILES(smiles=<SMILES>)
      获取CID
    • 调用
      PubChem_get_compound_properties_by_CID(cid=<CID>)
      获取化合物名称及MW
    • 将输入的SMILES用于后续所有ADMETAI调用
  3. 记录:
    • 化合物名称、CID、SMILES、分子式、分子量、IUPAC名称
    • 若CID查询失败，仅使用SMILES继续（ADMETAI不需要CID）
重要性：ADMETAI工具需要SMILES作为输入。PubChemTox工具使用CID时效果最佳。同时确认两者可确保所有下游工具都能被调用。PubChem是SMILES规范化的权威来源。
备选方案：若PubChem无相关条目，用户必须直接提供SMILES。无SMILES则无法继续分析。

PHASE 2: Physicochemical Properties & Drug-Likeness

阶段2：物理化学性质与类药性

Goal: Evaluate whether the compound has drug-like physicochemical properties.
Steps:
  1. ADMETAI physicochemical (primary):
    ADMETAI_predict_physicochemical_properties(smiles=["<SMILES>"])
    Returns: MW, logP, TPSA, HBD, HBA, rotatable bonds
  2. SwissADME drug-likeness (complementary):
    SwissADME_check_druglikeness(operation="check_druglikeness", smiles="<SMILES>")
SwissADME_calculate_adme(operation="calculate_adme", smiles="<SMILES>")
    Returns: Lipinski, Veber, Ghose, Egan, Muegge rule compliance; PAINS alerts; Brenk alerts
  3. ADMETAI solubility:
    ADMETAI_predict_solubility_lipophilicity_hydration(smiles=["<SMILES>"])
    Returns: Aqueous solubility (LogS), lipophilicity, hydration free energy
Interpret & Score:
PropertyIdeal RangeWhy It Matters
MW< 500 DaLarger molecules have poor membrane permeability (Lipinski)
LogP-0.4 to 5.6Too hydrophobic = poor solubility; too hydrophilic = poor permeability
HBD<= 5Excess donors reduce membrane crossing (Lipinski)
HBA<= 10Excess acceptors reduce membrane crossing (Lipinski)
TPSA< 140 A^2High PSA correlates with poor oral absorption
Rotatable bonds<= 10Molecular flexibility affects bioavailability (Veber)
LogS> -6Below -6 = practically insoluble, formulation challenge
PAINS alerts0Pan-assay interference compounds give false positives in screens
Verdict: PASS if Lipinski <= 1 violation and no PAINS alerts; WARN if 2 violations; FAIL if 3+ violations or PAINS+.
Fallback: If ADMETAI import fails (missing 
tooluniverse[ml]
), rely on SwissADME alone. SwissADME provides all Lipinski descriptors independently.
目标：评估化合物是否具有类药物的物理化学性质。
步骤:
  1. ADMETAI物理化学分析（主要方法）:
    ADMETAI_predict_physicochemical_properties(smiles=["<SMILES>"])
    返回：MW、logP、TPSA、HBD、HBA、可旋转键数量
  2. SwissADME类药性分析（补充方法）:
    SwissADME_check_druglikeness(operation="check_druglikeness", smiles="<SMILES>")
SwissADME_calculate_adme(operation="calculate_adme", smiles="<SMILES>")
    返回：Lipinski、Veber、Ghose、Egan、Muegge规则合规性；PAINS警示；Brenk警示
  3. ADMETAI溶解度分析:
    ADMETAI_predict_solubility_lipophilicity_hydration(smiles=["<SMILES>"])
    返回：水溶性（LogS）、亲脂性、水合自由能
解读与评分:
属性理想范围重要性
MW< 500 Da分子越大，膜通透性越差（Lipinski规则）
LogP-0.4 至 5.6疏水性过强=溶解度差；亲水性过强=通透性差
HBD<= 5氢键供体过多会降低膜穿透性（Lipinski规则）
HBA<= 10氢键受体过多会降低膜穿透性（Lipinski规则）
TPSA< 140 A^2高TPSA与口服吸收差相关
可旋转键<= 10分子灵活性影响生物利用度（Veber规则）
LogS> -6低于-6=实际不溶，制剂难度大
PAINS警示0泛筛选干扰化合物会在筛选中产生假阳性
结论：若Lipinski规则违反<=1条且无PAINS警示，判定为PASS；违反2条判定为WARN；违反3条及以上或存在PAINS警示判定为FAIL。
备选方案：若ADMETAI导入失败（缺少
tooluniverse[ml]
），仅依赖SwissADME。SwissADME可独立提供所有Lipinski描述符。

PHASE 3: ADME Predictions

阶段3：ADME预测

Goal: Predict absorption, distribution, metabolism, and excretion behavior.
Steps:
  1. Blood-brain barrier penetration:
    ADMETAI_predict_BBB_penetrance(smiles=["<SMILES>"])
    • BBB+ = compound can cross; BBB- = cannot
    • Critical for CNS drugs (must cross) and peripherally-acting drugs (should NOT cross to avoid CNS side effects)
  2. Oral bioavailability:
    ADMETAI_predict_bioavailability(smiles=["<SMILES>"])
    • F20% = at least 20% oral bioavailability; F30% = at least 30%
    • Low bioavailability means the drug is extensively metabolized or poorly absorbed
    • F < 20% generally requires non-oral routes (IV, inhaled, topical)
  3. CYP450 interactions:
    ADMETAI_predict_CYP_interactions(smiles=["<SMILES>"])
    • Reports substrate/inhibitor status for CYP1A2, 2C9, 2C19, 2D6, 3A4
    • Why CYP matters: ~75% of drugs are metabolized by CYP enzymes. Inhibiting CYP3A4 (which metabolizes ~50% of drugs) causes dangerous drug-drug interactions (DDIs). CYP2D6 polymorphisms affect ~25% of drugs -- poor metabolizers accumulate toxic levels
    • Substrate of CYP2D6 = pharmacogenomic risk (poor/ultra-rapid metabolizers)
    • Inhibitor of CYP3A4 = high DDI risk (co-administered drugs accumulate)
  4. Clearance and distribution:
    ADMETAI_predict_clearance_distribution(smiles=["<SMILES>"])
    • VDss (volume of distribution): low (<0.7 L/kg) = confined to plasma; high (>1 L/kg) = distributed to tissues
    • Clearance: high clearance = short half-life, frequent dosing needed
    • Plasma protein binding (PPB): >95% bound = narrow therapeutic window, DDI risk from displacement
  5. SwissADME pharmacokinetics (cross-validation):
    • GI absorption (high/low), P-gp substrate status, skin permeation (logKp)
Key flags: BBB+ for non-CNS drug (WARN: CNS side effects); BBB- for CNS drug (FAIL: won't reach target); F < 20% (WARN: poor oral bioavailability); CYP3A4 inhibitor (WARN: high DDI); CYP2D6 substrate (WARN: pharmacogenomic variability); PPB > 99% (WARN: narrow window); high clearance + low bioavailability (FAIL).
Fallback: If ADMETAI unavailable, SwissADME provides GI absorption, BBB permeation (yes/no), P-gp substrate, and CYP inhibition predictions.
目标：预测吸收、分布、代谢、排泄行为。
步骤:
  1. 血脑屏障穿透性:
    ADMETAI_predict_BBB_penetrance(smiles=["<SMILES>"])
    • BBB+ = 化合物可穿透；BBB- = 无法穿透
    • 对中枢神经系统（CNS）药物至关重要（必须穿透），对外周作用药物也很重要（应避免穿透以减少CNS副作用）
  2. 口服生物利用度:
    ADMETAI_predict_bioavailability(smiles=["<SMILES>"])
    • F20% = 口服生物利用度至少20%；F30% = 至少30%
    • 生物利用度低意味着药物被大量代谢或吸收差
    • F < 20%通常需要非口服给药途径（静脉注射、吸入、外用）
  3. CYP450相互作用:
    ADMETAI_predict_CYP_interactions(smiles=["<SMILES>"])
    • 报告CYP1A2、2C9、2C19、2D6、3A4的底物/抑制剂状态
    • CYP的重要性：约75%的药物由CYP酶代谢。抑制CYP3A4（代谢约50%的药物）会导致危险的药物相互作用（DDIs）。CYP2D6基因多态性影响约25%的药物——代谢不良者会积累毒性水平
    • CYP2D6底物=药物基因组风险（代谢不良/超快代谢者）
    • CYP3A4抑制剂=高DDI风险（联合用药时药物会积累）
  4. 清除率与分布:
    ADMETAI_predict_clearance_distribution(smiles=["<SMILES>"])
    • VDss（分布容积）：低(<0.7 L/kg)=局限于血浆；高(>1 L/kg)=分布至组织
    • 清除率：高清除率=半衰期短，需频繁给药
    • 血浆蛋白结合率（PPB）：>95%结合=治疗窗窄，存在药物置换导致的DDI风险
  5. SwissADME药代动力学分析（交叉验证）:
    • 胃肠道吸收（高/低）、P-糖蛋白底物状态、皮肤渗透性（logKp）
关键标记：非CNS药物标记BBB+（WARN：CNS副作用）；CNS药物标记BBB-（FAIL：无法抵达靶点）；F < 20%（WARN：口服生物利用度差）；CYP3A4抑制剂（WARN：高DDI风险）；CYP2D6底物（WARN：药物基因组变异性）；PPB > 99%（WARN：治疗窗窄）；高清除率+低生物利用度（FAIL）。
备选方案：若ADMETAI不可用，SwissADME可提供胃肠道吸收、血脑屏障穿透性（是/否）、P-糖蛋白底物及CYP抑制预测。

PHASE 4: Toxicity Assessment

阶段4：毒性评估

Goal: Evaluate safety liabilities from both predicted and experimental sources.
Steps:
  1. ADMETAI toxicity predictions [T3]:
    ADMETAI_predict_toxicity(smiles=["<SMILES>"])
    Key endpoints:
    • AMES: Mutagenicity (bacterial reverse mutation test). Positive = potential carcinogen; regulatory agencies require AMES testing for all new drugs
    • DILI: Drug-induced liver injury risk. Leading cause of drug withdrawal (e.g., troglitazone). Positive = hepatotoxicity concern requiring liver function monitoring
    • hERG: hERG potassium channel inhibition. Causes QT prolongation and fatal cardiac arrhythmia. hERG+ = cardiotoxicity liability; multiple drugs withdrawn for this (e.g., terfenadine, cisapride)
    • ClinTox: Clinical trial toxicity / FDA withdrawal risk. Trained on drugs that failed trials or were withdrawn for toxicity
    • LD50_Zhu: Predicted lethal dose (mg/kg, rat oral). Lower = more acutely toxic
    • Skin_Reaction: Dermal sensitization potential. Important for topical drugs
    • Carcinogens_Lagunin: Carcinogenicity prediction
  2. Nuclear receptor activity [T3]:
    ADMETAI_predict_nuclear_receptor_activity(smiles=["<SMILES>"])
    • AR (androgen receptor), ER (estrogen receptor), AhR, PPAR-gamma activity
    • Positive = potential endocrine disruption; critical for chronic-use drugs and environmental chemicals
  3. Stress response pathways [T3]:
    ADMETAI_predict_stress_response(smiles=["<SMILES>"])
    • p53 activation = DNA damage response (genotoxicity signal)
    • MMP disruption = mitochondrial toxicity
    • ATAD5 = DNA repair stress
    • HSE = heat shock / protein misfolding stress
  4. PubChemTox experimental data [T2] (call all in parallel):
    PubChemTox_get_toxicity_values(cid=<CID>)
PubChemTox_get_ghs_classification(cid=<CID>)
PubChemTox_get_acute_effects(cid=<CID>)
PubChemTox_get_carcinogen_classification(cid=<CID>)
PubChemTox_get_target_organs(cid=<CID>)
PubChemTox_get_toxicity_summary(cid=<CID>)
    • Real animal study data (LD50, LC50, NOAEL) anchors computational predictions
    • GHS classification provides internationally harmonized hazard categories
    • Carcinogen classification from IARC (Group 1/2A/2B), NTP, EPA
Key flags: AMES positive (FAIL: mutagenic); DILI positive (WARN: hepatotox); hERG positive (FAIL: cardiac, often program-killing); ClinTox positive (WARN); LD50 < 50 mg/kg (FAIL: GHS 1-2); LD50 50-300 mg/kg (WARN: GHS 3); NR-ER/AR active (WARN: endocrine disruption); p53 active (WARN: genotoxicity); IARC Group 1/2A (FAIL: known/probable carcinogen).
Fallback: If ADMETAI unavailable, PubChemTox provides experimental toxicity data for known compounds. For novel compounds without PubChem entries, flag as "no experimental toxicity data available -- computational predictions only."
目标：结合预测与实验来源评估安全风险。
步骤:
  1. ADMETAI毒性预测 [T3]:
    ADMETAI_predict_toxicity(smiles=["<SMILES>"])
    关键终点:
    • AMES：致突变性（细菌回复突变试验）。阳性=潜在致癌物；监管机构要求所有新药进行AMES测试
    • DILI：药物诱导肝损伤风险。是药物撤市的主要原因（例如：曲格列酮）。阳性=需监测肝功能的肝毒性风险
    • hERG：hERG钾通道抑制。会导致QT间期延长和致命性心律失常。hERG+ = 心脏毒性风险；多个药物因此撤市（例如：特非那定、西沙必利）
    • ClinTox：临床试验毒性/FDA撤市风险。基于因毒性失败或撤市的药物训练模型
    • LD50_Zhu：预测致死剂量（mg/kg，大鼠口服）。数值越低=急性毒性越强
    • Skin_Reaction：皮肤致敏潜力。对外用药物很重要
    • Carcinogens_Lagunin：致癌性预测
  2. 核受体活性 [T3]:
    ADMETAI_predict_nuclear_receptor_activity(smiles=["<SMILES>"])
    • AR（雄激素受体）、ER（雌激素受体）、AhR、PPAR-γ活性
    • 阳性=潜在内分泌干扰；对长期使用药物和环境化学品至关重要
  3. 应激反应通路 [T3]:
    ADMETAI_predict_stress_response(smiles=["<SMILES>"])
    • p53激活=DNA损伤反应（遗传毒性信号）
    • MMP破坏=线粒体毒性
    • ATAD5=DNA修复应激
    • HSE=热休克/蛋白错误折叠应激
  4. PubChemTox实验数据 [T2]（并行调用所有接口）:
    PubChemTox_get_toxicity_values(cid=<CID>)
PubChemTox_get_ghs_classification(cid=<CID>)
PubChemTox_get_acute_effects(cid=<CID>)
PubChemTox_get_carcinogen_classification(cid=<CID>)
PubChemTox_get_target_organs(cid=<CID>)
PubChemTox_get_toxicity_summary(cid=<CID>)
    • 真实动物研究数据（LD50、LC50、NOAEL）为计算预测提供锚点
    • GHS分类提供国际统一的危害类别
    • IARC（1/2A/2B组）、NTP、EPA的致癌物分类
关键标记：AMES阳性（FAIL：致突变）；DILI阳性（WARN：肝毒性）；hERG阳性（FAIL：心脏毒性，常导致项目终止）；ClinTox阳性（WARN）；LD50 < 50 mg/kg（FAIL：GHS 1-2类）；LD50 50-300 mg/kg（WARN：GHS 3类）；NR-ER/AR活性阳性（WARN：内分泌干扰）；p53活性阳性（WARN：遗传毒性）；IARC 1/2A组（FAIL：已知/疑似致癌物）。
备选方案：若ADMETAI不可用，PubChemTox可为已知化合物提供实验毒性数据。对于无PubChem条目的新型化合物，标记为“无实验毒性数据可用——仅基于计算预测”。

PHASE 5: Scorecard Assembly & Clinical Context

阶段5：评分卡汇总与临床背景

Goal: Aggregate all findings into a structured ADMET scorecard with pass/warn/fail verdicts.
Steps:
  1. ChEMBL clinical status [T1] (if drug has ChEMBL ID):
    ChEMBL_get_molecule(chembl_id="<CHEMBL_ID>")
    • Max phase: 4 = approved, 3 = Phase III, 2 = Phase II, 1 = Phase I, 0 = preclinical
    • Ro5 violations from ChEMBL (independent validation of Lipinski)
    • First approval year, indication class, black box warning flag
  2. Build the ADMET Scorecard: produce a table with 13 categories (Physicochemical, Solubility, Absorption, Distribution, Metabolism, Excretion, Tox: Mutagenicity/Hepatotoxicity/Cardiotoxicity/Carcinogenicity/Acute, Endocrine, Clinical Tox), each with PASS/WARN/FAIL verdict and key finding. Include compound identity header and overall verdict. Tag each finding with evidence tier [T1-T3].
  3. Interpretation narrative: After the scorecard, provide a 3-5 sentence summary:
    • Highlight the most critical findings (any FAILs or WARNs)
    • State whether the compound is suitable for oral administration
    • Note any DDI risks from CYP interactions
    • Flag pharmacogenomic concerns (CYP2D6 substrate)
    • Recommend next steps (e.g., "hERG patch clamp assay recommended to confirm computational prediction")
目标：将所有发现整合为结构化ADMET评分卡，包含通过/警告/失败结论。
步骤:
  1. ChEMBL临床状态 [T1]（若药物有ChEMBL ID）:
    ChEMBL_get_molecule(chembl_id="<CHEMBL_ID>")
    • 最高阶段：4=已获批，3=III期，2=II期，1=I期，0=临床前
    • ChEMBL中的Ro5违反情况（Lipinski规则的独立验证）
    • 首次获批年份、适应症类别、黑框警告标记
  2. 构建ADMET评分卡：生成包含13个类别的表格（物理化学性质、溶解度、吸收、分布、代谢、排泄、毒性：致突变性/肝毒性/心脏毒性/致癌性/急性毒性、内分泌毒性、临床毒性），每个类别包含PASS/WARN/FAIL结论及关键发现。包含化合物身份标题和总体结论。为每个发现标记证据层级[T1-T3]。
  3. 解读说明：评分卡后提供3-5句总结：
    • 突出最关键的发现（任何FAIL或WARN）
    • 说明化合物是否适合口服给药
    • 标注CYP相互作用导致的DDI风险
    • 标记药物基因组学问题（CYP2D6底物）
    • 建议下一步行动（例如：“建议进行hERG膜片钳实验以确认计算预测结果”）

Completeness Checklist (MANDATORY before reporting)

完整性检查清单（报告前必须完成）

Before delivering the final scorecard, verify:
  • Compound identity resolved (name, CID, SMILES all present or explicitly noted as unavailable)
  • Physicochemical properties reported with Lipinski verdict
  • At least one source for each ADME property (ADMETAI or SwissADME)
  • All 7 ADMETAI toxicity endpoints reported (or marked N/A with reason)
  • PubChemTox experimental data checked (even if "no data found")
  • Nuclear receptor and stress response checked (or marked N/A)
  • Evidence tier tagged for every finding
  • Scorecard table complete with verdicts for all 13 categories
  • Overall verdict stated
  • Interpretation narrative provided with actionable next steps
在提交最终评分卡前，验证：
  • 化合物身份已确认（名称、CID、SMILES均已获取或明确标注不可用）
  • 已报告物理化学性质及Lipinski结论
  • 每个ADME属性至少有一个来源（ADMETAI或SwissADME）
  • 已报告所有7项ADMETAI毒性终点（或标记为N/A并说明原因）
  • 已检查PubChemTox实验数据（即使显示“未找到数据”）
  • 已检查核受体及应激反应（或标记为N/A）
  • 每个发现均标记了证据层级
  • 评分卡表格完整，包含所有13个类别的结论
  • 已给出总体结论
  • 已提供带有可操作下一步建议的解读说明