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:
- Predictive Toxicology: AI-predicted endpoints (AMES, DILI, LD50, carcinogenicity, hERG) via SMILES
- ADMET Profiling: Absorption, distribution, metabolism, excretion, toxicity
- Toxicogenomics: Chemical-gene-disease mapping from CTD
- Regulatory Safety: FDA label warnings, contraindications, adverse reactions
- Drug Safety: DrugBank safety + FDA labels combined
- Chemical-Protein Interactions: STITCH-based interaction networks
- 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
- Report-first approach - Create report file FIRST, then populate progressively
- Tool parameter verification - Verify params via
get_tool_info before calling unfamiliar tools
- Evidence grading - Grade all safety claims by evidence strength (T1-T4)
- Citation requirements - Every toxicity finding must have inline source attribution
- Mandatory completeness - All sections must exist with data or explicit "No data" notes
- Disambiguation first - Resolve compound identity (name -> SMILES, CID, ChEMBL ID) before analysis
- Negative results documented - "No toxicity signals found" is data; empty sections are failures
- Conservative risk assessment - When evidence is ambiguous, flag as "requires further investigation"
- English-first queries - Always use English chemical/drug names in tool calls
Evidence Grading System (MANDATORY)
| Tier |
Symbol |
Criteria |
Examples |
| T1 |
[T1] |
Direct human evidence, regulatory finding |
FDA boxed warning, clinical trial toxicity |
| T2 |
[T2] |
Animal studies, validated in vitro |
Nonclinical toxicology, AMES positive, animal LD50 |
| T3 |
[T3] |
Computational prediction, association data |
ADMET-AI prediction, CTD association |
| T4 |
[T4] |
Database annotation, text-mined |
Literature mention, unvalidated database entry |
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
See phase-procedures-detailed.md for 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 require pip 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 take smiles: list[str])
Phase 3: Toxicogenomics
CTD_get_chemical_gene_interactions (input_terms: str) — chemical name, returns gene interactions across speciesCTD_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 valuesPubChemTox_get_ghs_classification (cid: int) — GHS hazard classification and pictogramsPubChemTox_get_carcinogen_classification (cid: int) — NTP/IARC carcinogenicity assessmentsPubChemTox_get_acute_effects (cid: int) — acute toxicity by route/speciesPubChemTox_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/mechanismAOPWiki_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 take drug_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_partners for 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
| Risk Level |
Criteria |
| CRITICAL |
FDA boxed warning OR multiple [T1] toxicity findings OR active DILI + active hERG |
| HIGH |
FDA warnings OR [T2] animal toxicity OR multiple active ADMET endpoints |
| MEDIUM |
Some [T3] predictions positive OR CTD disease associations OR structural alerts |
| LOW |
All ADMET endpoints negative AND no FDA/DrugBank flags AND no CTD concerns |
| INSUFFICIENT DATA |
Fewer than 3 phases returned data |
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
See report-templates.md for full section templates with example tables.
Mandatory Completeness Checklist
Common Use Patterns
- Novel Compound: SMILES -> Phase 0 (resolve) -> Phase 1 (toxicity) -> Phase 2 (ADMET) -> Phase 7 (structural alerts) -> Synthesis
- Approved Drug Review: Drug name -> All phases (0-7) -> Complete safety dossier
- Environmental Chemical: Chemical name -> Phase 0 -> Phase 1-2 -> Phase 3 (CTD, key) -> Phase 6 (STITCH) -> Synthesis
- Batch Screening: Multiple SMILES -> Phase 0 -> Phase 1-2 (batch) -> Comparative table -> Synthesis
- 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
