From tooluniverse
Investigates drugs using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature. Generates structured reports with evidence grading and citations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tooluniverse:tooluniverse-drug-researchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.
Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.
KEY PRINCIPLES:
When asked about a drug, query ChEMBL/PubChem/DailyMed FIRST. Don't guess at mechanism, targets, or side effects — look them up. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.
When investigating a drug's mechanism of action, trace the full causal chain:
DO NOT show the search process or tool outputs to the user. Instead:
[DRUG]_drug_report.md with all 11 section headers and [Researching...] placeholders. See REPORT_TEMPLATE.md for the full template.Every piece of information MUST include its source. Use inline citations:
*Source: PubChem via `PubChem_get_compound_properties_by_CID` (CID: 4091)*
Step 1: Create report file with all section headers
Step 2: Resolve compound identifiers -> Update Section 1
Step 3: Query PubChem/ADMET-AI/DailyMed SPL -> Update Section 2 (Chemistry)
Step 4: Query FDA Label MOA + ChEMBL + DGIdb -> Update Section 3 (Mechanism)
Step 5: Query ADMET-AI tools -> Update Section 4 (ADMET)
Step 6: Query ClinicalTrials.gov -> Update Section 5 (Clinical)
Step 7: Query FAERS/DailyMed -> Update Section 6 (Safety)
Step 8: Query PharmGKB -> Update Section 7 (Pharmacogenomics)
Step 9: Query DailyMed/Orange Book -> Update Section 8 (Regulatory)
Step 10: Query PubMed/literature -> Update Section 9 (Literature)
Step 11: Synthesize findings -> Update Executive Summary & Section 10
Step 12: Document all sources -> Update Section 11 (Data Sources)
CRITICAL: Establish compound identity before any research.
1. PubChem_get_CID_by_compound_name(compound_name)
-> Extract: CID, canonical SMILES, formula
2. ChEMBL_search_compounds(query=drug_name)
-> Extract: ChEMBL ID, pref_name
3. DailyMed_search_spls(drug_name)
-> Extract: Set ID, NDC codes (if approved)
4. PharmGKB_search_drugs(query=drug_name)
-> Extract: PharmGKB ID (PA...)
| Issue | Example | Resolution |
|---|---|---|
| Salt forms | metformin vs metformin HCl | Note all CIDs; use parent compound |
| Isomers | omeprazole vs esomeprazole | Verify SMILES; separate entries if distinct |
| Prodrugs | enalapril vs enalaprilat | Document both; note conversion |
| Brand confusion | Different products same name | Clarify with user |
Each path has detailed tool chains and output examples in REPORT_GUIDELINES.md.
Tools: PubChem properties -> ADMET-AI physicochemical -> ADMET-AI solubility -> DailyMed chemistry/description Output: Physicochemical table, Lipinski assessment, QED score, salt forms, formulation comparison
Tools: DailyMed MOA -> ChEMBL activities (NOT ChEMBL_get_molecule_targets) -> ChEMBL target details -> DGIdb -> PubChem bioactivity
Critical: Derive targets from activities filtered to pChEMBL >= 6.0. Avoid ChEMBL_get_molecule_targets.
Output: FDA MOA text, target table with UniProt/potency, selectivity profile
Tools: ADMET-AI (bioavailability, BBB, CYP, clearance, toxicity) Fallback: DailyMed clinical_pharmacology + pharmacokinetics + drug_interactions Critical: If ADMET-AI fails, automatically use fallback. Never leave Section 4 empty.
Tools: search_clinical_trials -> compute phase counts -> extract outcomes/AEs -> fda_pharmacogenomic_biomarkers Critical: Section 5.2 must show actual counts by phase/status in table format.
Tools: FAERS (reactions, seriousness, outcomes, deaths, age) + DailyMed (DDI, dosing, warnings) Critical: Include FAERS date window, seriousness breakdown, and limitations paragraph.
Tools: PharmGKB (search -> details -> annotations -> guidelines) Fallback: DailyMed pharmacogenomics section + PubMed literature
Tools: FDA Orange Book (search, approval history, exclusivity, patents, generics) + DailyMed (special populations via LOINC codes) Note: US-only data; document EMA/PMDA limitation.
Tools: ClinicalTrials.gov (OBSERVATIONAL studies) + PubMed (real-world, registry, surveillance)
Tools: Abbreviated tool chains for each comparator + head-to-head trial search + PubMed meta-analyses
For approved drugs, retrieve these DailyMed sections early (after getting set_id):
| Batch | Sections | Maps to Report |
|---|---|---|
| Phase 1 | mechanism_of_action, pharmacodynamics, chemistry | Sections 2-3 |
| Phase 2 | clinical_pharmacology, pharmacokinetics, drug_interactions | Sections 4, 6.5 |
| Phase 3 | warnings_and_cautions, adverse_reactions, dosage_and_administration | Sections 6, 8.2 |
| Phase 4 | pharmacogenomics, clinical_studies, description, inactive_ingredients | Sections 5, 7 |
| Primary Tool | Fallback | Use When |
|---|---|---|
PubChem_get_CID_by_compound_name | ChEMBL_search_drugs | Name not in PubChem |
ChEMBL_get_molecule_targets | Use ChEMBL_search_activities instead | Always avoid this tool |
ChEMBL_get_activity | PubChemBioAssay_get_assay_summary | No ChEMBL ID |
DailyMed_search_spls | PubChemTox_get_acute_effects | DailyMed timeout |
PharmGKB_search_drugs | DailyMed PGx sections + PubMed | PharmGKB unavailable |
PharmGKB_get_dosing_guidelines | DailyMed pharmacogenomics section | PharmGKB API error |
FAERS_count_reactions_by_drug_event | Document "FAERS unavailable" + use label AEs | API error |
ADMETAI_* (all tools) | DailyMed clinical_pharmacology + pharmacokinetics | Invalid SMILES or API error |
| Use Case | Primary Tool | Fallback | Evidence |
|---|---|---|---|
| Name -> CID | PubChem_get_CID_by_compound_name | ChEMBL_search_drugs | T1 |
| Properties | PubChem_get_compound_properties_by_CID | ADMET-AI physicochemical | T1/T2 |
| FDA MOA | DailyMed_parse_clinical_pharmacology (mechanism_of_action) | - | T1 |
| Targets | ChEMBL_search_activities -> ChEMBL_get_target | DGIdb_get_drug_info | T1 |
| ADMET | ADMETAI_predict_* (5 tools) | DailyMed PK sections | T2/T1 |
| Trials | search_clinical_trials | - | T1 |
| Trial outcomes | extract_clinical_trial_outcomes | - | T1 |
| FAERS | FAERS_count_reactions_by_drug_event | Label adverse_reactions | T1 |
| Dose mods | DailyMed_parse_clinical_pharmacology (dosage, warnings) | - | T1 |
| PGx | PharmGKB_search_drugs | DailyMed PGx + PubMed | T2/T1 |
| Label | DailyMed_search_spls | PubChemTox_get_acute_effects | T1 |
| Literature | PubMed_search_articles | EuropePMC_search_articles | Varies |
| Regulatory | FDA_OrangeBook_* tools | DailyMed label data | T1 |
See TOOLS_REFERENCE.md for the complete tool listing with parameters and input format requirements.
Many tools require string inputs. Always convert IDs before API calls:
["SMILES_STRING"]"METFORMIN")"CHEMBL1431" not "1431""PA450657" not "450657"| Use Case | Primary Sections | Light Sections |
|---|---|---|
| Approved Drug Profile | All 11 sections | None |
| Investigational Compound | 1, 2, 3, 4, 9 | 5, 6, 7, 8 |
| Safety Review | 1, 5, 6, 7, 9 | 2, 3, 4, 8 |
| ADMET Assessment | 1, 2, 4 | 3, 5, 6, 7, 8, 9 |
| Clinical Development Landscape | 1, 5, 9 | 2, 3, 4, 6, 7, 8 |
Always maintain all section headers but adjust depth based on query focus and data availability.
PubChem_search_compounds_by_similarity directlyFor drug interaction checking, run: python3 skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py --type interaction --drug1 X --drug2 Y
npx claudepluginhub mims-harvard/tooluniverse --plugin tooluniverseTraces drug mechanism of action from primary target to clinical outcome using DrugBank, ChEMBL, KEGG, Reactome, STRING. Useful for understanding drug action, off-target effects, and combination therapy design.
Access and analyze DrugBank data — drug properties, interactions, targets, pathways, chemical structures, and pharmacology. Useful for pharmaceutical data, drug discovery, DDI analysis, and target identification.
Queries Open Targets Platform GraphQL API for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, and known drugs.