Summary
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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
- - Verify params via before calling unfamiliar tools
Tool parameter verification
get_tool_info
Evidence grading - Grade all safety claims by evidence strength (T1-T4)Citation requirements - Every toxicity finding must have inline source attributionMandatory completeness - All sections must exist with data minimums or explicit "No data" notesDisambiguation first - Resolve compound identity (name -> SMILES, CID, ChEMBL ID) before analysisNegative results documented - "No toxicity signals found" is data; empty sections are failuresConservative 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
| Property | Value |
|----------|-------|
| **Name** | Acetaminophen |
| **PubChem CID** | 1983 |
| **SMILES** | CC(=O)Nc1ccc(O)cc1 |
| **Formula** | C8H9NO2 |
| **Molecular Weight** | 151.16 |
| **InChI** | InChI=1S/C8H9NO2/... |
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]
| Endpoint | Prediction | Interpretation | Concern Level |
|----------|-----------|---------------|---------------|
| AMES Mutagenicity | Inactive | No mutagenic signal | Low |
| Carcinogenicity | Inactive | No carcinogenic signal | Low |
| ClinTox | Active | Clinical toxicity signal | HIGH |
| DILI | Active | Drug-induced liver injury risk | HIGH |
| LD50 (Zhu) | 2.45 log(mg/kg) | ~282 mg/kg (moderate) | Medium |
| Skin Reaction | Inactive | No skin sensitization signal | Low |
| hERG Inhibition | Active | Cardiac arrhythmia risk | HIGH |
*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
| Property | Value | Interpretation |
|----------|-------|----------------|
| BBB Penetrance | Yes | Crosses blood-brain barrier |
| Bioavailability (F20%) | 85% | Good oral absorption |
#### Distribution
| Property | Value | Interpretation |
|----------|-------|----------------|
| VDss | 1.2 L/kg | Moderate tissue distribution |
| PPB | 92% | Highly protein bound |
#### Metabolism
| CYP Enzyme | Substrate | Inhibitor |
|------------|-----------|-----------|
| CYP1A2 | No | No |
| CYP2C9 | Yes | No |
| CYP2C19 | No | No |
| CYP2D6 | No | No |
| CYP3A4 | Yes | Yes (DDI risk) |
#### Excretion
| Property | Value | Interpretation |
|----------|-------|----------------|
| Clearance | 8.5 mL/min/kg | Moderate clearance |
| Half-life | 6.2 h | Moderate half-life |
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)
| Gene | Interaction | Type | Evidence |
|------|------------|------|----------|
| CYP1A2 | increases expression | mRNA | [T2] curated |
| TP53 | affects activity | protein | [T2] curated |
| ... | ... | ... | ... |
**Total interactions found**: 156
**Top affected pathways**: Xenobiotic metabolism, Apoptosis, DNA damage response
#### Chemical-Disease Associations (Top 10)
| Disease | Association Type | Evidence |
|---------|-----------------|----------|
| Liver Neoplasms | marker/mechanism | [T2] curated |
| Contact Dermatitis | therapeutic | [T2] curated |
| ... | ... | ... |
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)
| Reaction | Frequency | Severity |
|----------|-----------|----------|
| Nausea | Common (>1%) | Mild |
| Hepatotoxicity | Rare (<0.1%) | Severe |
| ... | ... | ... |
#### 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
| Dimension | Finding | Evidence Tier | Concern |
|-----------|---------|--------------|---------|
| ADMET Toxicity | DILI active, hERG active | [T3] | HIGH |
| FDA Label | Boxed warning for hepatotoxicity | [T1] | CRITICAL |
| CTD Toxicogenomics | 156 gene interactions, liver neoplasms | [T2] | HIGH |
| DrugBank | Known hepatotoxicity at high doses | [T2] | HIGH |
| STITCH | Binds CYP3A4, hERG | [T3] | MEDIUM |
| Structural Alerts | 2 Brenk alerts | [T3] | MEDIUM |
### 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:
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
