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以下为本文档的中文说明

tooluniverse-chemical-safety（化学安全工具宇宙）是由哈佛大学医学院 MIMS（生物医学信息学）实验室开发的一个专注于化学物质安全数据查询和危险评估的专业技能。该技能整合了多个权威的化学安全数据库，为化学、生物、制药和环境等领域的科研人员和工业从业者提供便捷、全面、可靠的化学安全信息咨询。使用场景包括：在实验设计阶段查询将要使用化学物质的物理化学性质参数、毒性等级和安全操作规程以制定实验安全方案、评估工业生产过程或实验室操作中涉及的化学品的火灾、爆炸和健康危害风险、在化学品泄漏、火灾等安全事故应急响应中快速查询和获取关键的安全处置信息和急救指导、为新化学品的采购、入库存储和日常使用提供科学的安全存储建议。核心特点包括：整合查询多个国际权威化学安全数据源的分类信息，包括联合国 GHS（全球化学品统一分类和标签制度）的危险分类和象形图、美国 NFPA 704（美国消防协会）的菱形标识（健康/燃烧/反应/特殊危害）、以及通过 CAS 号（美国化学文摘社登记号）精确检索化学品的唯一标识信息；提供化学品全面的危险分类细节和危险性说明（H 代码），包括易燃性（闪点、自燃温度）、急性毒性（LD50/LC50 口服/吸入/经皮）、皮肤和眼腐蚀性/刺激性、严重危害水生环境等；完整的安全数据表（SDS/MSDS）核心内容自动摘要提取，包括第 4 部分急救措施（眼睛接触、皮肤接触、吸入、食入后的处理）、第 5 部分消防措施（适用灭火介质、防护装备、有害燃烧产物）、第 6 部分泄漏应急处理（个人预防措施、环境保护措施、收容和清除方法）；支持化学结构式（SMILES 字符串）和系统命名/IUPAC 命名/IUPAC 名称的灵活搜索；提供化学物质间的兼容性矩阵查询，为安全存储（隔离储存条件）和正确混合使用提供关键决策支持。该技能是实验室安全管理和工业过程安全管理的重要参考工具。

Chemical Safety & Toxicology Assessment

Toxicity assessment: identify the chemical, check known hazards (GHS, IARC), then look for ADMET predictions. Dose makes the poison — always consider exposure level, as a compound that is toxic at high doses may be safe at relevant exposures. Distinguish between acute toxicity (LD50, GHS category) and chronic hazards (carcinogenicity, endocrine disruption) — they require different risk management approaches. Computational predictions (ADMETAI) are T3 evidence and must be anchored by experimental data from PubChemTox or FDA labels wherever available. When evidence conflicts between prediction and experiment, always defer to the experimental finding.

LOOK UP DON’T GUESS: never assume GHS categories, IARC classification, or CTD disease links — always call PubChemTox and CTD tools to retrieve current classifications before reporting.

Comprehensive chemical safety analysis integrating predictive AI models, curated toxicogenomics databases, regulatory safety data, and chemical-biological interaction networks.

When to Use This Skill

Triggers:

• “Is this chemical toxic?” / “Assess the safety profile of [drug/chemical]”
• “What are the ADMET properties of [SMILES]?”
• “What genes does [chemical] interact with?” / “What diseases are linked to [chemical] exposure?”
• “Drug safety assessment” / “Environmental health risk” / “Chemical hazard profiling”

Use Cases:

1. Predictive Toxicology: AI-predicted endpoints (AMES, DILI, LD50, carcinogenicity, hERG) via SMILES
2. ADMET Profiling: Absorption, distribution, metabolism, excretion, toxicity
3. Toxicogenomics: Chemical-gene-disease mapping from CTD
4. Regulatory Safety: FDA label warnings, contraindications, adverse reactions
5. Drug Safety: DrugBank safety + FDA labels combined
6. Chemical-Protein Interactions: STITCH-based interaction networks
7. Environmental Toxicology: Chemical-disease associations for contaminants

COMPUTE, DON’T DESCRIBE

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.

KEY PRINCIPLES

1. Report-first approach- Create report file FIRST, then populate progressively
2. Tool parameter verification- Verify params viaget_tool_infobefore calling unfamiliar tools
3. Evidence grading- Grade all safety claims by evidence strength (T1-T4)
4. Citation requirements- Every toxicity finding must have inline source attribution
5. Mandatory completeness- All sections must exist with data or explicit “No data” notes
6. Disambiguation first- Resolve compound identity (name -> SMILES, CID, ChEMBL ID) before analysis
7. Negative results documented- “No toxicity signals found” is data; empty sections are failures
8. Conservative risk assessment- When evidence is ambiguous, flag as “requires further investigation”
9. English-first queries- Always use English chemical/drug names in tool calls

Evidence Grading System (MANDATORY)

Evidence grades MUST appear in: Executive Summary, Toxicity Predictions, Regulatory Safety, Chemical-Gene Interactions, Risk Assessment.

Core Strategy: 8 Research Phases

Chemical/Drug Query | +-- PHASE 0: Compound Disambiguation (ALWAYS FIRST) | Resolve name -> SMILES, PubChem CID, ChEMBL ID, formula, weight | +-- PHASE 1: Predictive Toxicology (ADMET-AI) | AMES, DILI, ClinTox, carcinogenicity, LD50, hERG, skin reaction | Stress response pathways, nuclear receptor activity | +-- PHASE 2: ADMET Properties | BBB penetrance, bioavailability, clearance, CYP interactions, physicochemical | +-- PHASE 3: Toxicogenomics (CTD) | Chemical-gene interactions, chemical-disease associations | +-- PHASE 4: Regulatory Safety (FDA Labels) | Boxed warnings, contraindications, adverse reactions, nonclinical tox | +-- PHASE 5: Drug Safety Profile (DrugBank) | Toxicity data, contraindications, drug interactions | +-- PHASE 6: Chemical-Protein Interactions (STITCH) | Direct binding, off-target effects, interaction confidence | +-- PHASE 7: Structural Alerts (ChEMBL) | PAINS, Brenk, Glaxo structural alerts | +-- SYNTHESIS: Integrated Risk Assessment Risk classification, evidence summary, data gaps, recommendations

Seephase-procedures-detailed.mdfor complete tool parameters, decision logic, output templates, and fallback strategies for each phase.

Tool Summary by Phase

Phase 0: Compound Disambiguation

• PubChem_get_CID_by_compound_name(name: str)
• PubChem_get_compound_properties_by_CID(cid: int)
• ChEMBL_get_molecule(if ChEMBL ID available)

Phase 1: Predictive Toxicology

Dependency: ADMET-AI tools requirepip install tooluniverse[ml]. If unavailable, skip to Phase 3 and use CTD + PubChemTox as alternatives.

• ADMETAI_predict_toxicity(smiles: list[str]) - AMES, DILI, ClinTox, LD50, hERG, etc.
• ADMETAI_predict_stress_response(smiles: list[str])
• ADMETAI_predict_nuclear_receptor_activity(smiles: list[str])

Phase 2: ADMET Properties

• ADMETAI_predict_BBB_penetrance/_bioavailability/_clearance_distribution/_CYP_interactions/_physicochemical_properties/_solubility_lipophilicity_hydration(all takesmiles: list[str])

Phase 3: Toxicogenomics

• CTD_get_chemical_gene_interactions(input_terms: str) — chemical name, returns gene interactions across species
• CTD_get_chemical_diseases(input_terms: str) — chemical-disease associations with evidence type

Phase 3.5: PubChem Toxicity Data

• PubChemTox_get_toxicity_values(cid: int) — LD50, LC50, NOAEL reference values
• PubChemTox_get_ghs_classification(cid: int) — GHS hazard classification and pictograms
• PubChemTox_get_carcinogen_classification(cid: int) — NTP/IARC carcinogenicity assessments
• PubChemTox_get_acute_effects(cid: int) — acute toxicity by route/species
• PubChemTox_get_toxicity_summary(cid: int) — integrated toxicity overview

Phase 3.6: Adverse Outcome Pathways

• AOPWiki_list_aops(keyword: str) — search for relevant AOPs by chemical/mechanism
• AOPWiki_get_aop(aop_id: int) — full AOP detail: MIE, key events, adverse outcome

Phase 4: Regulatory Safety (for pharmaceuticals only)

Environmental chemicals: Skip Phases 4-5 (no FDA labels/DrugBank). Use CTD + PubChemTox + AOPWiki instead.

• FDA_get_boxed_warning_info_by_drug_name/_contraindications_/_adverse_reactions_/_warnings_(all takedrug_name: str)

Phase 5: Drug Safety (for pharmaceuticals only)

• drugbank_get_safety_by_drug_name_or_drugbank_id(query,case_sensitive,exact_match,limit- all 4 required)

Phase 6: Chemical-Protein Interactions

• STITCH_get_chemical_protein_interactions(identifiers: list[str],species: int)
• Fallback(if STITCH fails for industrial chemicals):STRING_get_interaction_partnersfor key target genes (e.g., ESR1 for endocrine disruptors)
• DGIdb_get_drug_gene_interactions(genes: list[str]) — for target druggability context

Phase 7: Structural Alerts

• ChEMBL_search_compound_structural_alerts(molecule_chembl_id: str)

Risk Classification Matrix

Report Structure

# Chemical Safety & Toxicology Report: [Compound Name] **Generated**: YYYY-MM-DD | **SMILES**: [...] | **CID**: [...] ## Executive Summary (risk classification + key findings, all graded) ## 1. Compound Identity (disambiguation table) ## 2. Predictive Toxicology (ADMET-AI endpoints) ## 3. ADMET Profile (absorption, distribution, metabolism, excretion) ## 4. Toxicogenomics (CTD chemical-gene-disease) ## 5. Regulatory Safety (FDA label data) ## 6. Drug Safety Profile (DrugBank) ## 7. Chemical-Protein Interactions (STITCH network) ## 8. Structural Alerts (ChEMBL) ## 9. Integrated Risk Assessment (classification, evidence summary, gaps, recommendations) ## Appendix: Methods and Data Sources

Seereport-templates.mdfor full section templates with example tables.

Mandatory Completeness Checklist

• Phase 0: Compound disambiguated (SMILES + CID minimum)
• Phase 1: At least 5 toxicity endpoints or “prediction unavailable”
• Phase 2: ADMET A/D/M/E sections or “not available”
• Phase 3: CTD queried; results or “no data in CTD”
• Phase 4: FDA labels queried; results or “not FDA-approved”
• Phase 5: DrugBank queried; results or “not found”
• Phase 6: STITCH queried; results or “no data available”
• Phase 7: Structural alerts checked or “ChEMBL ID not available”
• Synthesis: Risk classification with evidence summary
• Evidence Grading: All findings have [T1]-[T4] annotations
• Data Gaps: Explicitly listed

Common Use Patterns

1. Novel Compound: SMILES -> Phase 0 (resolve) -> Phase 1 (toxicity) -> Phase 2 (ADMET) -> Phase 7 (structural alerts) -> Synthesis
2. Approved Drug Review: Drug name -> All phases (0-7) -> Complete safety dossier
3. Environmental Chemical: Chemical name -> Phase 0 -> Phase 1-2 -> Phase 3 (CTD, key) -> Phase 6 (STITCH) -> Synthesis
4. Batch Screening: Multiple SMILES -> Phase 0 -> Phase 1-2 (batch) -> Comparative table -> Synthesis
5. Toxicogenomic Deep-Dive: Chemical + gene/disease interest -> Phase 0 -> Phase 3 (expanded CTD) -> Literature -> Synthesis

Limitations

• ADMET-AI: Computational [T3]; should not replace experimental testing
• CTD: May lag behind latest literature by 6-12 months
• FDA: Only covers FDA-approved drugs; not applicable to environmental chemicals
• DrugBank: Primarily drugs; limited industrial chemical coverage
• STITCH: Lower score thresholds increase false positives
• ChEMBL: Structural alerts require ChEMBL ID; not all compounds have one
• Novel compounds: May only have ADMET-AI predictions (no database evidence)
• SMILES validity: Invalid SMILES cause ADMET-AI failures

Reference Files

• phase-procedures-detailed.md- Complete tool parameters, decision logic, output templates, fallback strategies per phase
• evidence-grading.md- Evidence grading details and examples
• report-templates.md- Full report section templates with example tables
• phase-details.md- Additional phase context
• test_skill.py- Test suite

Summary

Total tools integrated: 25+ tools across 6 databases (ADMET-AI, CTD, FDA, DrugBank, STITCH, ChEMBL)

Best for: Drug safety assessment, chemical hazard profiling, environmental toxicology, ADMET characterization, toxicogenomic analysis

Outputs: Structured markdown report with risk classification (Critical/High/Medium/Low), evidence grading [T1-T4], and actionable recommendations