Drug Research Strategy
Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.
KEY PRINCIPLES:
- •Report-first approach - Create report file FIRST, then populate progressively
- •Compound disambiguation FIRST - Resolve identifiers before research
- •Citation requirements - Every fact must have inline source attribution
- •Evidence grading - Grade claims by evidence strength
- •Mandatory completeness - All sections must exist, even if "data unavailable"
Critical Workflow Requirements
1. Report-First Approach (MANDATORY)
DO NOT show the search process or tool outputs to the user. Instead:
- •
Create the report file FIRST - Before any data collection, create a markdown file:
- •File name:
[DRUG]_drug_report.md(e.g.,metformin_drug_report.md) - •Initialize with all 11 section headers from the template
- •Add placeholder text:
[Researching...]in each section
- •File name:
- •
Progressively update the report - As you gather data:
- •Update each section with findings immediately after retrieving data
- •Replace
[Researching...]with actual content - •The user sees the report growing, not the search process
- •
Use ALL relevant tools - For comprehensive coverage:
- •Query multiple databases for each data type
- •Cross-reference information across sources
- •Use fallback tools when primary tools return limited data
2. Citation Requirements (MANDATORY)
Every piece of information MUST include its source. Use inline citations:
markdown
## 3. Mechanism & Targets ### 3.1 Primary Mechanism Metformin activates AMP-activated protein kinase (AMPK), reducing hepatic glucose production and increasing insulin sensitivity in peripheral tissues. *Source: PubChem via `PubChem_get_drug_label_info_by_CID` (CID: 4091)* ### 3.2 Primary Target(s) | Target | UniProt | Activity | Potency | Source | |--------|---------|----------|---------|--------| | AMPK (PRKAA1) | Q13131 | Activator | EC50 ~10 µM | ChEMBL | | Mitochondrial Complex I | - | Inhibitor | IC50 ~1 mM | Literature | *Source: ChEMBL via `ChEMBL_get_target_by_chemblid` (CHEMBL1431)*
Citation Format
For each data section, include at the end:
markdown
--- **Data Sources for this section:** - PubChem: `PubChem_get_compound_properties_by_CID` (CID: 4091) - ChEMBL: `ChEMBL_get_bioactivity_by_chemblid` (CHEMBL1431) - DGIdb: `DGIdb_get_drug_info` (metformin) ---
3. Progressive Writing Workflow
code
Step 1: Create report file with all section headers
↓
Step 2: Resolve compound identifiers → Update Section 1
↓
Step 3: Query PubChem/ADMET-AI → Update Section 2 (Chemistry)
↓
Step 4: Query ChEMBL/DGIdb → Update Section 3 (Mechanism & Targets)
↓
Step 5: Query ADMET-AI tools → Update Section 4 (ADMET)
↓
Step 6: Query ClinicalTrials.gov → Update Section 5 (Clinical Development)
↓
Step 7: Query FAERS/DailyMed → Update Section 6 (Safety)
↓
Step 8: Query PharmGKB → Update Section 7 (Pharmacogenomics)
↓
Step 9: Query DailyMed → 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)
4. Report Detail Requirements
Each section must be comprehensive and detailed:
- •Tables: Use tables for structured data (targets, trials, adverse events)
- •Lists: Use bullet points for features, findings, key points
- •Paragraphs: Include narrative summaries that synthesize findings
- •Numbers: Include specific values, counts, percentages (not vague terms)
- •Context: Explain what the data means, not just what it is
BAD (too brief):
markdown
### Clinical Trials Multiple trials completed. Approved for diabetes.
GOOD (detailed with sources):
markdown
### 5.2 Clinical Trial Landscape | Phase | Total | Completed | Recruiting | Status | |-------|-------|-----------|------------|--------| | Phase 4 | 89 | 72 | 12 | Post-marketing | | Phase 3 | 156 | 134 | 15 | Pivotal | | Phase 2 | 203 | 178 | 18 | Dose-finding | | Phase 1 | 67 | 61 | 4 | Safety | *Source: ClinicalTrials.gov via `search_clinical_trials` (intervention="metformin")* **Total Registered Trials**: 515 (as of 2026-02-04) **Primary Indications Under Investigation**: Type 2 diabetes (312), PCOS (87), Cancer (45), Obesity (38), NAFLD (33) ### Trial Outcomes Summary - **Glycemic Control**: Mean HbA1c reduction of 1.0-1.5% in monotherapy [★★★: NCT00123456] - **Cardiovascular**: UKPDS showed 39% reduction in MI risk [★★★: PMID:9742976] - **Cancer Prevention**: Mixed results; ongoing investigation [★★☆: NCT02019979] *Source: `extract_clinical_trial_outcomes` for NCT IDs listed*
Initial Report Template (Create This First)
When starting research, immediately create this file before any tool calls:
File: [DRUG]_drug_report.md
markdown
# Drug Research Report: [DRUG NAME] **Generated**: [Date] | **Query**: [Original query] | **Status**: In Progress --- ## Executive Summary [Researching...] --- ## 1. Compound Identity ### 1.1 Database Identifiers [Researching...] ### 1.2 Structural Information [Researching...] ### 1.3 Names & Synonyms [Researching...] --- ## 2. Chemical Properties ### 2.1 Physicochemical Profile [Researching...] ### 2.2 Drug-Likeness Assessment [Researching...] ### 2.3 Solubility & Permeability [Researching...] --- ## 3. Mechanism & Targets ### 3.1 Primary Mechanism of Action [Researching...] ### 3.2 Primary Target(s) [Researching...] ### 3.3 Target Selectivity & Off-Targets [Researching...] ### 3.4 Bioactivity Profile (ChEMBL) [Researching...] --- ## 4. ADMET Properties ### 4.1 Absorption [Researching...] ### 4.2 Distribution [Researching...] ### 4.3 Metabolism [Researching...] ### 4.4 Excretion [Researching...] ### 4.5 Toxicity Predictions [Researching...] --- ## 5. Clinical Development ### 5.1 Development Status [Researching...] ### 5.2 Clinical Trial Landscape [Researching...] ### 5.3 Approved Indications [Researching...] ### 5.4 Investigational Indications [Researching...] ### 5.5 Key Efficacy Data [Researching...] --- ## 6. Safety Profile ### 6.1 Clinical Adverse Events [Researching...] ### 6.2 Post-Marketing Safety (FAERS) [Researching...] ### 6.3 Black Box Warnings [Researching...] ### 6.4 Contraindications [Researching...] ### 6.5 Drug-Drug Interactions [Researching...] ### 6.6 Drug-Target Interactions (Off-Target) [Researching...] --- ## 7. Pharmacogenomics ### 7.1 Relevant Pharmacogenes [Researching...] ### 7.2 Clinical Annotations [Researching...] ### 7.3 Dosing Guidelines (CPIC/DPWG) [Researching...] ### 7.4 Actionable Variants [Researching...] --- ## 8. Regulatory & Labeling ### 8.1 Approval Status [Researching...] ### 8.2 Label Highlights [Researching...] ### 8.3 Patents & Exclusivity [Researching...] --- ## 9. Literature & Research Landscape ### 9.1 Publication Metrics [Researching...] ### 9.2 Research Themes [Researching...] ### 9.3 Recent Key Publications [Researching...] --- ## 10. Conclusions & Assessment ### 10.1 Drug Profile Scorecard [Researching...] ### 10.2 Key Strengths [Researching...] ### 10.3 Key Concerns/Limitations [Researching...] ### 10.4 Research Gaps [Researching...] --- ## 11. Data Sources & Methodology [Will be populated as research progresses...]
Then progressively replace [Researching...] with actual findings as you query each tool.
Compound Disambiguation (Phase 1)
CRITICAL: Establish compound identity before any research.
Identifier Resolution Chain
code
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...)
Handle Naming Ambiguity
| 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 |
Tool Chains by Research Path
PATH 1: Chemical Properties
Objective: Full physicochemical profile and drug-likeness
Multi-Step Chain:
code
1. PubChem_get_compound_properties_by_CID(cid) └─ Extract: MW, formula, XLogP, TPSA, HBD, HBA, rotatable bonds 2. ADMETAI_predict_physicochemical_properties(smiles=[smiles]) └─ Extract: MW, logP, HBD, HBA, Lipinski, QED, stereo_centers, TPSA 3. ADMETAI_predict_solubility_lipophilicity_hydration(smiles=[smiles]) └─ Extract: Solubility_AqSolDB, Lipophilicity_AstraZeneca
Output for Report:
markdown
### 2.1 Physicochemical Profile | Property | Value | Drug-Likeness | Source | |----------|-------|---------------|--------| | **Molecular Weight** | 129.16 g/mol | ✓ (< 500) | PubChem | | **LogP** | -2.64 | ✓ (< 5) | ADMET-AI | | **TPSA** | 91.5 Ų | ✓ (< 140) | PubChem | | **H-Bond Donors** | 2 | ✓ (≤ 5) | PubChem | | **H-Bond Acceptors** | 5 | ✓ (< 10) | PubChem | | **Rotatable Bonds** | 2 | ✓ (< 10) | PubChem | **Lipinski Rule of Five**: ✓ PASS (0 violations) **QED Score**: 0.74 (Good drug-likeness) *Sources: PubChem via `PubChem_get_compound_properties_by_CID`, ADMET-AI via `ADMETAI_predict_physicochemical_properties`*
PATH 2: Targets & Bioactivity
Objective: Primary targets, mechanism, selectivity, drug-protein interactions
Multi-Step Chain:
code
1. ChEMBL_get_bioactivity_by_chemblid(chembl_id) └─ Extract: Target names, activity types, potency values [★★★] 2. ChEMBL_get_target_by_chemblid(chembl_id) └─ Extract: Target ChEMBL IDs, UniProt accessions [★★★] 3. DGIdb_get_drug_info(drugs=[drug_name]) └─ Extract: Target genes, interaction types, sources [★★☆] 4. PubChem_get_bioactivity_summary_by_CID(cid) └─ Extract: Assay summary, active/inactive counts [★★☆] 5. STITCH_get_chemical_protein_interactions(identifiers=[smiles], species=9606) └─ Extract: Predicted/known protein targets, confidence scores [★★☆-★☆☆] 6. STITCH_get_interaction_partners(identifiers=[drug_name], species=9606) └─ Extract: Full interaction network [★☆☆ for predictions]
Evidence Tier Guide for Targets:
- •ChEMBL binding assay (IC50 <100nM) = ★★★
- •ChEMBL functional assay = ★★☆
- •Predicted interaction (STITCH) = ★☆☆
Output for Report:
markdown
### 3.2 Primary Target(s) | Target | UniProt | Type | Potency | Assays | Source | |--------|---------|------|---------|--------|--------| | PRKAA1 (AMPK α1) | Q13131 | Activator | EC50 ~10 µM | 12 | ChEMBL | | PRKAA2 (AMPK α2) | P54646 | Activator | EC50 ~15 µM | 8 | ChEMBL | | SLC22A1 (OCT1) | O15245 | Substrate | Km ~1.5 mM | 5 | DGIdb | *Source: ChEMBL via `ChEMBL_get_target_by_chemblid` (CHEMBL1431)* ### 3.4 Bioactivity Profile **Total ChEMBL Activities**: 847 datapoints across 234 assays - **Potency Range**: IC50/EC50 from 1 µM to 10 mM - **Primary Activity**: AMPK activation (kinase assays) - **Secondary Activities**: Mitochondrial complex I inhibition *Source: `ChEMBL_get_bioactivity_by_chemblid`*
PATH 3: ADMET Properties
Objective: Full ADMET profile with predictions
Multi-Step Chain:
code
1. ADMETAI_predict_bioavailability(smiles=[smiles]) └─ Extract: Bioavailability_Ma, HIA_Hou, PAMPA_NCATS, Caco2_Wang, Pgp_Broccatelli 2. ADMETAI_predict_BBB_penetrance(smiles=[smiles]) └─ Extract: BBB_Martins (0-1 probability) 3. ADMETAI_predict_CYP_interactions(smiles=[smiles]) └─ Extract: CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4 (inhibitor/substrate) 4. ADMETAI_predict_clearance_distribution(smiles=[smiles]) └─ Extract: Clearance, Half_Life_Obach, VDss_Lombardo, PPBR_AZ 5. ADMETAI_predict_toxicity(smiles=[smiles]) └─ Extract: AMES, hERG, DILI, ClinTox, LD50_Zhu, Carcinogens
Output for Report:
markdown
### 4.1 Absorption | Endpoint | Prediction | Interpretation | |----------|------------|----------------| | **Oral Bioavailability** | 0.72 | Good (>50%) | | **Human Intestinal Absorption** | 0.89 | High | | **Caco-2 Permeability** | -5.2 (log cm/s) | Moderate | | **PAMPA** | 0.34 | Low-moderate | | **P-gp Substrate** | 0.23 | Unlikely substrate | *Source: ADMET-AI via `ADMETAI_predict_bioavailability`* ### 4.5 Toxicity Predictions | Endpoint | Prediction | Risk Level | |----------|------------|------------| | **AMES Mutagenicity** | 0.08 | Low risk | | **hERG Inhibition** | 0.12 | Low risk | | **Hepatotoxicity (DILI)** | 0.15 | Low risk | | **Clinical Toxicity** | 0.21 | Low risk | | **LD50** | 2.8 (log mol/kg) | Moderate | *Source: ADMET-AI via `ADMETAI_predict_toxicity`* **Summary**: Low predicted toxicity across all endpoints. Favorable safety profile.
PATH 4: Clinical Trials
Objective: Complete clinical development picture
Multi-Step Chain:
code
1. search_clinical_trials(intervention=drug_name, pageSize=100) └─ Extract: Total count, NCT IDs, phases, statuses 2. get_clinical_trial_conditions_and_interventions(nct_ids=[top_ids]) └─ Extract: Conditions, interventions, arm groups 3. extract_clinical_trial_outcomes(nct_ids=[completed_phase3]) └─ Extract: Primary outcomes, efficacy measures, p-values 4. extract_clinical_trial_adverse_events(nct_ids=[completed_ids]) └─ Extract: Serious AEs, common AEs by organ system
PATH 5: Post-Marketing Safety & Drug Interactions
Objective: Real-world safety signals from FAERS + drug-drug interactions
Multi-Step Chain (FAERS):
code
1. FAERS_count_reactions_by_drug_event(medicinalproduct=drug_name) └─ Extract: Top 20 adverse reactions by MedDRA term [★★★ real-world] 2. FAERS_count_seriousness_by_drug_event(medicinalproduct=drug_name) └─ Extract: Serious vs non-serious ratio [★★★] 3. FAERS_count_outcomes_by_drug_event(medicinalproduct=drug_name) └─ Extract: Recovered, recovering, fatal, unresolved counts [★★★] 4. FAERS_count_death_related_by_drug(medicinalproduct=drug_name) └─ Extract: Fatal outcome count [★★★] 5. FAERS_count_patient_age_distribution(medicinalproduct=drug_name) └─ Extract: Reports by age group [★★★]
Multi-Step Chain (Drug-Drug Interactions):
code
6. STITCH_get_chemical_protein_interactions(identifiers=[drug1, drug2], species=9606) └─ Extract: Shared targets (potential DDI mechanism) [★★☆] 7. DailyMed_search_spls(drug_name) └─ Extract: Drug interactions section from label [★★★ FDA-approved] 8. drugbank_get_targets_by_drug_name_or_drugbank_id(query=drug_name) └─ Extract: Targets, enzymes, transporters for DDI prediction [★★☆]
DDI Mechanism Analysis: For each major interaction found, note:
- •CYP enzyme involved (CYP3A4, CYP2D6, etc.)
- •Interaction type (inhibitor/inducer/substrate)
- •Clinical severity (contraindicated, major, moderate, minor)
Output for Report:
markdown
### 6.2 Post-Marketing Safety (FAERS) **Total FAERS Reports**: 45,234 (as of 2026-02-04) #### Top Adverse Reactions | Reaction (MedDRA PT) | Count | % of Reports | |----------------------|-------|--------------| | Diarrhoea | 8,234 | 18.2% | | Nausea | 6,892 | 15.2% | | Lactic acidosis | 3,456 | 7.6% | | Vomiting | 2,987 | 6.6% | | Abdominal pain | 2,543 | 5.6% | *Source: FDA FAERS via `FAERS_count_reactions_by_drug_event`* #### Outcome Distribution | Outcome | Count | Percentage | |---------|-------|------------| | Recovered/Resolved | 18,234 | 40.3% | | Not Recovered | 12,456 | 27.5% | | Fatal | 2,134 | 4.7% | | Unknown | 12,410 | 27.4% | *Source: `FAERS_count_outcomes_by_drug_event`* **Signal Assessment**: Lactic acidosis signal consistent with known labeling. GI events expected class effect.
PATH 6: Pharmacogenomics
Objective: PGx associations and dosing guidelines
Multi-Step Chain:
code
1. PharmGKB_search_drugs(query=drug_name) └─ Extract: PharmGKB drug ID 2. PharmGKB_get_drug_details(drug_id) └─ Extract: Cross-references, related genes 3. PharmGKB_get_clinical_annotations(gene_id) # For each related gene └─ Extract: Variant-drug associations, evidence levels 4. PharmGKB_get_dosing_guidelines(gene=gene_symbol) └─ Extract: CPIC/DPWG guideline recommendations
Output for Report:
markdown
### 7.1 Relevant Pharmacogenes | Gene | Role | Evidence Level | Source | |------|------|----------------|--------| | **SLC22A1** (OCT1) | Transporter (uptake) | 2A | PharmGKB | | **SLC22A2** (OCT2) | Transporter (renal) | 2B | PharmGKB | | **SLC47A1** (MATE1) | Transporter (efflux) | 3 | PharmGKB | *Source: PharmGKB via `PharmGKB_get_drug_details`* ### 7.3 Dosing Guidelines **CPIC Guideline**: No CPIC guideline currently available for metformin. **Clinical Annotations**: - rs628031 (SLC22A1): Reduced metformin response in *4/*4 carriers - rs316019 (SLC22A2): May affect renal clearance *Source: `PharmGKB_get_clinical_annotations`*
Evidence Grading System
Evidence Tiers
| Tier | Symbol | Description | Example |
|---|---|---|---|
| T1 | ★★★ | Phase 3 RCT, meta-analysis, FDA approval | Pivotal trial, label indication |
| T2 | ★★☆ | Phase 1/2 trial, large case series | Dose-finding study |
| T3 | ★☆☆ | In vivo animal, in vitro cellular | Mouse PK study |
| T4 | ☆☆☆ | Review mention, computational prediction | ADMET-AI prediction |
Application in Report
markdown
Metformin reduces hepatic glucose output via AMPK activation [★★★: FDA Label]. Phase 3 trials demonstrated HbA1c reduction of 1.0-1.5% [★★★: NCT00123456]. Preclinical studies suggest anti-cancer properties [★☆☆: PMID:23456789]. ADMET-AI predicts low hERG liability (0.12) [☆☆☆: computational].
Per-Section Summary
Include evidence quality summary for each major section:
markdown
### 5. Clinical Development **Evidence Quality**: Strong (156 Phase 3 trials, 203 Phase 2, 67 Phase 1) **Data Confidence**: High - mature clinical program with decades of data
Section Completeness Checklist
Before finalizing any report, verify each section meets minimum requirements:
Section 1 (Identity) - Minimum Requirements
- • PubChem CID with link
- • ChEMBL ID with link (or "Not in ChEMBL")
- • Canonical SMILES
- • Molecular formula and weight
- • At least 3 brand names OR "Generic only"
- • Salt forms identified (or "Parent compound only")
Section 2 (Chemistry) - Minimum Requirements
- • 6+ physicochemical properties in table format
- • Lipinski rule assessment with pass/fail
- • QED score with interpretation
- • Solubility prediction with interpretation
Section 3 (Mechanism) - Minimum Requirements
- • Primary mechanism described in 2-3 sentences
- • At least 1 primary target with UniProt ID
- • Activity type and potency (IC50/EC50/Ki)
- • Off-target activity addressed (or "Highly selective")
Section 4 (ADMET) - Minimum Requirements
- • All 5 subsections present (A, D, M, E, T)
- • Absorption: bioavailability + at least 2 other endpoints
- • Distribution: BBB + VDss or PPB
- • Metabolism: CYP substrate/inhibitor status for 3+ CYPs
- • Excretion: clearance OR half-life
- • Toxicity: AMES + hERG + at least 1 other
Section 5 (Clinical) - Minimum Requirements
- • Development status clearly stated (Approved/Investigational/Preclinical)
- • Trial counts by phase in table format
- • Approved indications with year (or "Not approved")
- • Key efficacy data with trial references (or "No outcome data")
Section 6 (Safety) - Minimum Requirements
- • Top 5 adverse events with frequencies
- • FAERS data OR explicit "Insufficient FAERS data"
- • Black box warnings (or "None")
- • At least 3 drug interactions OR "No significant interactions"
Section 7 (PGx) - Minimum Requirements
- • Pharmacogenes listed (or "None identified in PharmGKB")
- • CPIC/DPWG guideline status
- • At least 1 clinical annotation OR "No annotations"
Section 10 (Conclusions) - Minimum Requirements
- • 5-point scorecard covering: efficacy, safety, PK, druggability, competition
- • 3+ key strengths
- • 3+ key concerns/limitations
- • At least 2 research gaps identified
Drug Profile Scorecard Template
Include in Section 10:
markdown
### 10.1 Drug Profile Scorecard | Criterion | Score (1-5) | Rationale | |-----------|-------------|-----------| | **Efficacy Evidence** | 5 | Multiple Phase 3 trials, decades of use | | **Safety Profile** | 4 | Well-tolerated; lactic acidosis rare but serious | | **PK/ADMET** | 4 | Good bioavailability; renal elimination | | **Target Validation** | 4 | AMPK mechanism well-established | | **Competitive Position** | 3 | First-line but many alternatives | | **Overall** | 4.0 | **Strong drug profile** | **Interpretation**: - 5 = Excellent, 4 = Good, 3 = Moderate, 2 = Concerning, 1 = Poor
Fallback Chains
| Primary Tool | Fallback | Use When |
|---|---|---|
PubChem_get_CID_by_compound_name | ChEMBL_search_compounds | Name not in PubChem |
ChEMBL_get_bioactivity_by_chemblid | PubChem_get_bioactivity_summary_by_CID | No ChEMBL ID |
DailyMed_search_spls | PubChem_get_drug_label_info_by_CID | DailyMed timeout |
PharmGKB_get_dosing_guidelines | Document "No guideline" | No CPIC/DPWG |
FAERS_count_reactions_by_drug_event | Document "FAERS unavailable" | API error |
ADMETAI_* | Document "Predictions unavailable" | Invalid SMILES |
Quick Reference: Tools by Use Case
| Use Case | Primary Tool | Fallback | Evidence Tier |
|---|---|---|---|
| Name → CID | PubChem_get_CID_by_compound_name | ChEMBL_search_compounds | ★★★ |
| SMILES → CID | PubChem_get_CID_by_SMILES | - | ★★★ |
| Properties | PubChem_get_compound_properties_by_CID | ADMETAI_predict_physicochemical_properties | ★★★ / ★★☆ |
| Drug-likeness | ADMETAI_predict_physicochemical_properties | Calculate from properties | ★★☆ |
| Targets | ChEMBL_get_target_by_chemblid | DGIdb_get_drug_info | ★★★ |
| Predicted targets | STITCH_get_chemical_protein_interactions | - | ★☆☆ |
| Bioactivity | ChEMBL_get_bioactivity_by_chemblid | PubChem_get_bioactivity_summary_by_CID | ★★★ |
| Absorption | ADMETAI_predict_bioavailability | - | ★★☆ (predicted) |
| BBB | ADMETAI_predict_BBB_penetrance | - | ★★☆ (predicted) |
| CYP | ADMETAI_predict_CYP_interactions | - | ★★☆ (predicted) |
| Toxicity | ADMETAI_predict_toxicity | - | ★★☆ (predicted) |
| Drug interactions | DailyMed_search_spls | STITCH_* tools | ★★★ / ★★☆ |
| Trials | search_clinical_trials | - | ★★★ |
| Trial outcomes | extract_clinical_trial_outcomes | - | ★★★ |
| FAERS | FAERS_count_reactions_by_drug_event | - | ★★★ |
| Label | DailyMed_search_spls | PubChem_get_drug_label_info_by_CID | ★★★ |
| PGx | PharmGKB_search_drugs | - | ★★☆-★★★ |
| CPIC | PharmGKB_get_dosing_guidelines | - | ★★★ |
| Literature | PubMed_search_articles | EuropePMC_search_articles | Varies |
Common Use Cases
Approved Drug Profile
User: "Tell me about metformin" → Full 11-section report emphasizing clinical data, FAERS, PGx
Investigational Compound
User: "What do we know about compound X (ChEMBL123456)?" → Emphasize preclinical data, mechanism, early trials; safety sections may be sparse
Safety Review
User: "What are the safety concerns with drug Y?" → Deep dive on FAERS, black box warnings, interactions, PGx; lighter on chemistry
ADMET Assessment
User: "Evaluate this compound's drug-likeness [SMILES]" → Focus on Sections 2 and 4; other sections may be brief or N/A
Clinical Development Landscape
User: "What trials are ongoing for drug Z?" → Heavy emphasis on Section 5; trial tables with status, phases, indications
When NOT to Use This Skill
- •Target research → Use target-intelligence-gatherer skill
- •Disease research → Use disease-research skill
- •Literature-only → Use literature-deep-research skill
- •Single property lookup → Call tool directly
- •Structure similarity search → Use
PubChem_search_compounds_by_similaritydirectly
Use this skill for comprehensive, multi-dimensional drug profiling.
Additional Resources
- •Tool reference: TOOLS_REFERENCE.md - Complete tool listing
- •Verification checklist: CHECKLIST.md - Pre-delivery verification
- •Examples: EXAMPLES.md - Detailed workflow examples