Chemical Safety & Toxicology Assessment

Comprehensive chemical safety and toxicology analysis integrating predictive AI models, curated toxicogenomics databases, regulatory safety data, and chemical-biological interaction networks. Generates structured risk assessment reports with evidence grading.

When to Use This Skill

Triggers:

• "Is this chemical toxic?" / "What are the toxicity endpoints for [compound]?"

• "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?"

• "Predict toxicity for these molecules"

• "Drug safety assessment for [drug name]"

• "Environmental health risk of [chemical]"

• "Chemical hazard profiling"

• "Toxicogenomic analysis of [compound]"

Use Cases:

• Predictive Toxicology: AI-predicted toxicity endpoints (AMES mutagenicity, DILI, LD50, carcinogenicity, skin reactions) for novel compounds via SMILES

• ADMET Profiling: Full absorption, distribution, metabolism, excretion, toxicity characterization

• Toxicogenomics: Chemical-gene interaction mapping, gene-disease associations from CTD

• Regulatory Safety: FDA label warnings, boxed warnings, contraindications, adverse reactions

• Drug Safety Assessment: Combined DrugBank safety + FDA labels + adverse event data

• Chemical-Protein Interactions: STITCH-based chemical-protein binding and interaction networks

• Environmental Toxicology: Chemical-disease associations for environmental contaminants

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 minimums 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)

Grade every toxicity claim by evidence strength:

Tier Symbol Criteria Examples

T1 [T1] Direct human evidence, regulatory finding FDA boxed warning, clinical trial toxicity, human case reports

T2 [T2] Animal studies, validated in vitro Nonclinical toxicology, AMES positive, animal LD50

T3 [T3] Computational prediction, association data ADMET-AI prediction, CTD association, QSAR model

T4 [T4] Database annotation, text-mined Literature mention, database entry without validation

Required Evidence Grading Locations

Evidence grades MUST appear in:

• Executive Summary - Key toxicity findings graded

• Toxicity Predictions - Every ADMET-AI endpoint with confidence note

• Regulatory Safety - FDA findings marked [T1]

• Chemical-Gene Interactions - CTD data marked by curation status

• Risk Assessment - Final risk classification with supporting evidence tiers

Core Strategy: 8 Research Dimensions

Chemical/Drug Query | +-- PHASE 0: Compound Disambiguation (ALWAYS FIRST) | +-- Resolve name -> SMILES, PubChem CID, ChEMBL ID | +-- Get molecular formula, weight, canonical structure | +-- PHASE 1: Predictive Toxicology (ADMET-AI) | +-- Mutagenicity (AMES) | +-- Hepatotoxicity (DILI, ClinTox) | +-- Carcinogenicity | +-- Acute toxicity (LD50) | +-- Skin reactions | +-- Stress response pathways | +-- Nuclear receptor activity | +-- PHASE 2: ADMET Properties | +-- Absorption: BBB penetrance, bioavailability | +-- Distribution: clearance, volume of distribution | +-- Metabolism: CYP interactions (1A2, 2C9, 2C19, 2D6, 3A4) | +-- Physicochemical: solubility, lipophilicity, pKa | +-- PHASE 3: Toxicogenomics (CTD) | +-- Chemical-gene interactions | +-- Chemical-disease associations | +-- Affected biological pathways | +-- PHASE 4: Regulatory Safety (FDA Labels) | +-- Boxed warnings (Black Box) | +-- Contraindications | +-- Adverse reactions | +-- Warnings and precautions | +-- Nonclinical toxicology | +-- PHASE 5: Drug Safety Profile (DrugBank) | +-- Toxicity data | +-- Contraindications | +-- Drug interactions affecting safety | +-- PHASE 6: Chemical-Protein Interactions (STITCH) | +-- Direct chemical-protein binding | +-- Interaction confidence scores | +-- Off-target effects | +-- PHASE 7: Structural Alerts (ChEMBL) | +-- Known toxic substructures (PAINS, Brenk) | +-- Structural alert flags | +-- SYNTHESIS: Integrated Risk Assessment +-- Aggregate all evidence tiers +-- Risk classification (Low/Medium/High/Critical) +-- Data gaps and recommendations

Phase 0: Compound Disambiguation (ALWAYS FIRST)

CRITICAL: Resolve compound identity before any analysis.

Input Types Handled

Input Format Resolution Strategy

Drug name (e.g., "Aspirin") PubChem_get_CID_by_compound_name -> get SMILES from properties

SMILES string Use directly for ADMET-AI; resolve to CID for other tools

PubChem CID PubChem_get_compound_properties_by_CID -> get SMILES + name

ChEMBL ID ChEMBL_get_molecule -> get SMILES + properties

Resolution Steps

• Input detection: Determine if input is name, SMILES, CID, or ChEMBL ID

• SMILES: contains typical SMILES characters (=, #, [, ], (, ), c, n, o and no spaces in middle)

• CID: numeric only

• ChEMBL: starts with "CHEMBL"

• Otherwise: treat as compound name

• Name to CID: PubChem_get_CID_by_compound_name(name=<compound_name>)

• CID to properties: PubChem_get_compound_properties_by_CID(cid=<cid>)

• Extract SMILES: Get SMILES from PubChem properties (field: ConnectivitySMILES , CanonicalSMILES , or IsomericSMILES depending on response format)

• Store resolved IDs: Maintain dict with name , smiles , cid , formula , weight , inchi

Disambiguation Output

Compound Identity

Phase 1: Predictive Toxicology (ADMET-AI)

When: SMILES is available (from Phase 0 or provided directly)

Objective: Run comprehensive AI-predicted toxicity endpoints

Tools Used

All ADMET-AI tools take the same parameter format:

Tool Predicted Endpoints Parameter

ADMETAI_predict_toxicity

AMES, Carcinogens_Lagunin, ClinTox, DILI, LD50_Zhu, Skin_Reaction, hERG smiles : list[str]

ADMETAI_predict_stress_response

Stress response pathway activation (ARE, ATAD5, HSE, MMP, p53) smiles : list[str]

ADMETAI_predict_nuclear_receptor_activity

AhR, AR, ER, PPARg, Aromatase nuclear receptor activity smiles : list[str]

Workflow

• Call ADMETAI_predict_toxicity(smiles=[resolved_smiles])

• Call ADMETAI_predict_stress_response(smiles=[resolved_smiles])

• Call ADMETAI_predict_nuclear_receptor_activity(smiles=[resolved_smiles])

• For each endpoint, interpret prediction:

• Classification endpoints: Active (1) = toxic signal, Inactive (0) = no signal

• Regression endpoints (LD50): Report numerical value with context

• All predictions graded [T3] (computational prediction)

Decision Logic

• Multiple SMILES: Can batch up to ~10 SMILES in single call

• Failed prediction: If ADMET-AI fails, note "prediction unavailable" (don't fail entire report)

• Confidence: Note that AI predictions are [T3] evidence, not definitive

• hERG flag: If hERG = Active, flag prominently (cardiac safety risk)

• AMES flag: If AMES = Active, flag prominently (mutagenicity concern)

• DILI flag: If DILI = Active, flag prominently (liver toxicity concern)

Output Table

Toxicity Predictions [T3]

All predictions from ADMET-AI. Evidence tier: [T3] (computational prediction)

Phase 2: ADMET Properties

When: SMILES is available

Objective: Full ADMET characterization beyond toxicity

Tools Used

Tool Properties Predicted Parameter

ADMETAI_predict_BBB_penetrance

Blood-brain barrier crossing probability smiles : list[str]

ADMETAI_predict_bioavailability

Oral bioavailability (F20%, F30%) smiles : list[str]

ADMETAI_predict_clearance_distribution

Clearance, VDss, half-life, PPB smiles : list[str]

ADMETAI_predict_CYP_interactions

CYP1A2, 2C9, 2C19, 2D6, 3A4 inhibition/substrate smiles : list[str]

ADMETAI_predict_physicochemical_properties

LogP, LogD, LogS, MW, pKa smiles : list[str]

ADMETAI_predict_solubility_lipophilicity_hydration

Aqueous solubility, lipophilicity, hydration free energy smiles : list[str]

Workflow

• Call all 6 ADMET tools in parallel (independent calls)

• Compile results into Absorption / Distribution / Metabolism / Excretion sections

• Assess Lipinski Rule of 5 compliance from physicochemical properties

• Flag drug-drug interaction risks from CYP inhibition profiles

Decision Logic

• BBB penetrant + toxicity: If BBB = Yes and any CNS toxicity endpoint active, flag as neurotoxicity risk

• Low bioavailability: If F20% = Low, note absorption concerns

• CYP inhibitor: If CYP3A4 inhibitor = Yes, flag high DDI risk

• Lipinski violations: Count violations and report drug-likeness assessment

Output Format

ADMET Profile [T3]

Absorption

Distribution

Metabolism

Excretion

Phase 3: Toxicogenomics (CTD)

When: Compound name is resolved

Objective: Map chemical-gene-disease relationships from curated CTD data

Tools Used

Tool Function Parameter

CTD_get_chemical_gene_interactions

Genes affected by chemical input_terms : str (chemical name)

CTD_get_chemical_diseases

Diseases linked to chemical exposure input_terms : str (chemical name)

Workflow

• Call CTD_get_chemical_gene_interactions(input_terms=compound_name)

• Call CTD_get_chemical_diseases(input_terms=compound_name)

• Parse gene interactions: extract gene symbols, interaction types (increases/decreases expression, binding, etc.)

• Parse disease associations: extract disease names, evidence types (marker/mechanism/therapeutic)

• Identify most affected biological processes from gene list

Decision Logic

• Direct evidence vs inferred: CTD separates curated direct evidence from inferred associations

• Therapeutic vs toxic: Disease associations can be therapeutic (drug treats disease) or adverse (chemical causes disease)

• Gene interaction types: Distinguish between expression changes, binding, and activity modulation

• Prioritize marker/mechanism: These indicate stronger causal evidence than simple associations

• Grade curated as [T2]: Direct curated CTD evidence from literature

• Grade inferred as [T3]: Computationally inferred associations

Output Format

Toxicogenomics (CTD) [T2/T3]

Chemical-Gene Interactions (Top 20)

Total interactions found: 156 Top affected pathways: Xenobiotic metabolism, Apoptosis, DNA damage response

Chemical-Disease Associations (Top 10)

Phase 4: Regulatory Safety (FDA Labels)

When: Compound has an approved drug name

Objective: Extract regulatory safety information from FDA drug labels

Tools Used

Tool Information Retrieved Parameter

FDA_get_boxed_warning_info_by_drug_name

Black box warnings (most serious) drug_name : str

FDA_get_contraindications_by_drug_name

Absolute contraindications drug_name : str

FDA_get_adverse_reactions_by_drug_name

Known adverse reactions drug_name : str

FDA_get_warnings_by_drug_name

Warnings and precautions drug_name : str

FDA_get_nonclinical_toxicology_info_by_drug_name

Animal toxicology data drug_name : str

FDA_get_carcinogenic_mutagenic_fertility_by_drug_name

Carcinogenicity/mutagenicity/fertility data drug_name : str

Workflow

• Call all 6 FDA tools in parallel (independent queries by drug name)

• Parse and structure each response

• Prioritize: Boxed Warnings > Contraindications > Warnings > Adverse Reactions

• All FDA label data is [T1] evidence (regulatory finding based on human/animal data)

Decision Logic

• Boxed warning present: Flag as CRITICAL safety concern in executive summary

• No FDA data: Chemical may not be an approved drug; note "Not an FDA-approved drug" and continue with other phases

• Multiple warnings: Categorize by organ system (hepatic, cardiac, renal, CNS, etc.)

• Nonclinical toxicology: Grade as [T2] (animal data supporting human risk)

Output Format

Regulatory Safety (FDA) [T1]

Boxed Warning

PRESENT - Hepatotoxicity risk with doses >4g/day. Liver failure reported. [T1]

Contraindications

• Severe hepatic impairment [T1]
• Known hypersensitivity [T1]

Adverse Reactions (by frequency)

Nonclinical Toxicology [T2]

• Carcinogenicity: No carcinogenic potential in 2-year rat/mouse studies
• Mutagenicity: Negative in Ames assay and in vivo micronucleus test
• Fertility: No effects on fertility at doses up to 10x human dose

Phase 5: Drug Safety Profile (DrugBank)

When: Compound is a known drug

Objective: Retrieve curated drug safety data from DrugBank

Tools Used

Tool Information Parameters

drugbank_get_safety_by_drug_name_or_drugbank_id

Toxicity, contraindications query : str, case_sensitive : bool, exact_match : bool, limit : int

Workflow

• Call drugbank_get_safety_by_drug_name_or_drugbank_id(query=drug_name, case_sensitive=False, exact_match=False, limit=5)

• Parse toxicity information, overdose data, contraindications

• Cross-reference with FDA data from Phase 4

Decision Logic

• Toxicity field: Contains LD50 values, overdose symptoms, organ toxicity data

• DrugBank ID: Note if found for cross-referencing

• Conflict with FDA: If DrugBank and FDA disagree, note discrepancy and defer to FDA [T1]

• Not found: Chemical may not be in DrugBank; continue with other phases

Phase 6: Chemical-Protein Interactions (STITCH)

When: Compound can be identified by name or SMILES

Objective: Map chemical-protein interaction network for off-target assessment

Tools Used

Tool Function Parameters

STITCH_resolve_identifier

Resolve chemical name to STITCH ID identifier : str, species : int (9606=human)

STITCH_get_chemical_protein_interactions

Get chemical-protein interactions identifiers : list[str], species : int, required_score : int

STITCH_get_interaction_partners

Get interaction network identifiers : list[str], species : int, limit : int

Workflow

• Resolve compound: STITCH_resolve_identifier(identifier=compound_name, species=9606)

• Get interactions: STITCH_get_chemical_protein_interactions(identifiers=[stitch_id], species=9606, required_score=700)

• Identify off-target proteins (not the intended drug target)

• Flag safety-relevant targets: hERG (cardiac), CYP enzymes (metabolism), nuclear receptors (endocrine)

Decision Logic

• High confidence (>900): Well-established interaction [T2]

• Medium confidence (700-900): Probable interaction [T3]

• Low confidence (400-700): Possible interaction, needs validation [T4]

• Safety-relevant targets: Flag interactions with known safety targets

• No STITCH data: Chemical may be too novel; note and continue

Phase 7: Structural Alerts (ChEMBL)

When: ChEMBL molecule ID is available (from Phase 0)

Objective: Check for known toxic substructures

Tools Used

Tool Function Parameters

ChEMBL_search_compound_structural_alerts

Find structural alert matches molecule_chembl_id : str, limit : int

Workflow

• If ChEMBL ID available: ChEMBL_search_compound_structural_alerts(molecule_chembl_id=chembl_id, limit=20)

• Parse alert types: PAINS (pan-assay interference), Brenk (medicinal chemistry), Glaxo (GSK structural alerts)

• Categorize severity: Some alerts are informational, others indicate likely toxicity

Decision Logic

• PAINS alerts: May cause false positives in screening; note for medicinal chemistry

• Brenk alerts: Known problematic substructures; flag if present

• No alerts: Good sign but not definitive proof of safety

• No ChEMBL ID: Skip this phase gracefully; note "structural alert analysis not available"

Synthesis: Integrated Risk Assessment (MANDATORY)

Always the final section. Integrates all evidence into actionable risk classification.

Risk Classification Matrix

Risk Level Criteria

CRITICAL FDA boxed warning present OR multiple [T1] toxicity findings OR active DILI + active hERG

HIGH FDA warnings present 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 safety flags AND no CTD concerns

INSUFFICIENT DATA Fewer than 3 phases returned data; cannot make confident assessment

Synthesis Template

Integrated Risk Assessment

Overall Risk Classification: [HIGH]

Evidence Summary

Key Safety Concerns

1. Hepatotoxicity [T1]: FDA boxed warning + ADMET-AI DILI prediction + CTD liver disease associations
2. Cardiac Risk [T3]: ADMET-AI hERG prediction + STITCH hERG interaction
3. Drug Interactions [T3]: CYP3A4 substrate/inhibitor, potential DDI risk

Data Gaps

• No in vivo genotoxicity data available
• STITCH interaction scores moderate (700-900)
• No environmental exposure data

Recommendations

1. Avoid doses >4g/day (hepatotoxicity threshold) [T1]
2. Monitor liver function in chronic use [T1]
3. Screen for CYP3A4 interactions before co-administration [T3]
4. Consider cardiac monitoring for at-risk patients [T3]

Mandatory Completeness Checklist

Before finalizing any report, verify:

• Phase 0: Compound fully disambiguated (SMILES + CID at minimum)

• Phase 1: At least 5 toxicity endpoints reported or "prediction unavailable" noted

• Phase 2: ADMET profile with A/D/M/E sections or "not available" noted

• Phase 3: CTD queried; gene interactions and disease associations reported or "no data in CTD"

• Phase 4: FDA labels queried; results or "not an FDA-approved drug" noted

• Phase 5: DrugBank queried; results or "not found in DrugBank" noted

• Phase 6: STITCH queried; results or "no STITCH data available" noted

• Phase 7: Structural alerts checked or "ChEMBL ID not available" noted

• Synthesis: Risk classification provided with evidence summary

• Evidence Grading: All findings have [T1]-[T4] annotations

• Data Gaps: Explicitly listed in synthesis section

Tool Parameter Reference

Critical Parameter Notes (verified from source code):

Tool Parameter Name Type Notes

All ADMETAI tools smiles

list[str]

Always a list, even for single compound

All CTD tools input_terms

str

Chemical name, MeSH name, CAS RN, or MeSH ID

All FDA tools drug_name

str

Brand or generic drug name

drugbank_get_safety_* query , case_sensitive , exact_match , limit

str, bool, bool, int All 4 required

STITCH_resolve_identifier identifier , species

str, int species=9606 for human

STITCH_get_chemical_protein_interactions identifiers , species , required_score

list[str], int, int required_score=400 default

PubChem_get_CID_by_compound_name name

str

Compound name (not SMILES)

PubChem_get_compound_properties_by_CID cid

int

Numeric CID

ChEMBL_search_compound_structural_alerts molecule_chembl_id

str

ChEMBL ID (e.g., "CHEMBL112")

Response Format Notes

• ADMET-AI: Returns {status: "success", data: {...}} with prediction values

• CTD: Returns list of interaction/association objects

• FDA: Returns {status, data} with label text

• DrugBank: Returns {data: [...]} with drug records

• STITCH: Returns list of interaction objects with scores

• PubChem CID lookup: Returns {IdentifierList: {CID: [...]}} (may or may not have data wrapper)

• PubChem properties: Returns dict with CID , MolecularWeight , ConnectivitySMILES , IUPACName

Fallback Strategies

Compound Resolution

• Primary: PubChem by name -> CID -> properties -> SMILES

• Fallback 1: ChEMBL search by name -> molecule -> SMILES

• Fallback 2: If SMILES provided directly, skip name resolution

Toxicity Prediction

• Primary: All 9 ADMET-AI endpoints

• Fallback: If ADMET-AI fails for a compound, note "prediction failed" and continue with database evidence

• Note: ADMET-AI may fail for very large or unusual SMILES

Regulatory Data

• Primary: FDA labels by drug name

• Fallback: If FDA returns no data, try alternative drug names (brand vs generic)

• Note: Non-drug chemicals (pesticides, industrial) will not have FDA labels

CTD Data

• Primary: Search by common chemical name

• Fallback: Try MeSH name if common name fails

• Note: Novel compounds may not be in CTD

Common Use Patterns

Pattern 1: Novel Compound Assessment

Input: SMILES string for new molecule Workflow: Phase 0 (SMILES->CID) -> Phase 1 (toxicity) -> Phase 2 (ADMET) -> Phase 7 (structural alerts) -> Synthesis Output: Predictive safety profile for novel compound

Pattern 2: Approved Drug Safety Review

Input: Drug name (e.g., "Acetaminophen") Workflow: All phases (0-7 + Synthesis) Output: Complete safety dossier with regulatory + predictive + database evidence

Pattern 3: Environmental Chemical Risk

Input: Chemical name (e.g., "Bisphenol A") Workflow: Phase 0 -> Phase 1 -> Phase 2 -> Phase 3 (CTD, key for env chemicals) -> Phase 6 -> Synthesis Output: Environmental health risk assessment focused on gene-disease associations

Pattern 4: Batch Toxicity Screening

Input: Multiple SMILES strings Workflow: Phase 0 -> Phase 1 (batch) -> Phase 2 (batch) -> Comparative table -> Synthesis Output: Comparative toxicity table ranking compounds by safety

Pattern 5: Toxicogenomic Deep-Dive

Input: Chemical name + specific gene or disease interest Workflow: Phase 0 -> Phase 3 (CTD expanded) -> Literature search -> Synthesis Output: Detailed chemical-gene-disease mechanistic analysis

Output Report Structure

All analyses generate a structured markdown report with progressive sections:

Chemical Safety & Toxicology Report: [Compound Name]

Generated: YYYY-MM-DD HH:MM Compound: [Name] | SMILES: [SMILES] | CID: [CID]

Executive Summary

[2-3 sentence overview with risk classification and key findings, all graded]

1. Compound Identity

[Phase 0 results - disambiguation table]

2. Predictive Toxicology

[Phase 1 results - ADMET-AI toxicity endpoints]

3. ADMET Profile

[Phase 2 results - absorption, distribution, metabolism, excretion]

4. Toxicogenomics

[Phase 3 results - CTD chemical-gene-disease relationships]

5. Regulatory Safety

[Phase 4 results - FDA label information]

6. Drug Safety Profile

[Phase 5 results - DrugBank data]

7. Chemical-Protein Interactions

[Phase 6 results - STITCH network]

8. Structural Alerts

[Phase 7 results - ChEMBL alerts]

9. Integrated Risk Assessment

[Synthesis - risk classification, evidence summary, data gaps, recommendations]

Appendix: Methods and Data Sources

[Tool versions, databases queried, date of access]

Limitations & Known Issues

Tool-Specific

• ADMET-AI: Predictions are computational [T3]; should not replace experimental testing

• CTD: Curated but may lag behind latest literature by 6-12 months

• FDA: Only covers FDA-approved drugs; not applicable to environmental chemicals or supplements

• DrugBank: Primarily drugs; limited coverage of industrial chemicals

• STITCH: Score thresholds affect sensitivity; lower scores increase false positives

• ChEMBL: Structural alerts require ChEMBL ID; not all compounds have one

Analysis

• Novel compounds: May only have ADMET-AI predictions (no database evidence)

• Environmental chemicals: FDA/DrugBank phases will be empty; rely on CTD and ADMET-AI

• Batch mode: ADMET-AI can handle batches; other tools require individual queries

• Species specificity: Most data is human-centric; animal data noted where applicable

Technical

• SMILES validity: Invalid SMILES will cause ADMET-AI failures

• Name ambiguity: Chemical names can be ambiguous; always verify with CID

• Rate limits: Some FDA endpoints may rate-limit for rapid queries

Summary

Chemical Safety & Toxicology Assessment Skill provides comprehensive safety evaluation by integrating:

• Predictive toxicology (ADMET-AI) - 9 tools covering toxicity, ADMET, physicochemical properties

• Toxicogenomics (CTD) - Chemical-gene-disease relationship mapping

• Regulatory safety (FDA) - 6 tools for label-based safety extraction

• Drug safety (DrugBank) - Curated toxicity and contraindication data

• Chemical interactions (STITCH) - Chemical-protein interaction networks

• Structural alerts (ChEMBL) - Known toxic substructure detection

Outputs: Structured markdown report with risk classification, evidence grading, and actionable recommendations

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

Total tools integrated: 25+ tools across 6 databases