Drug Research

Drug Research Strategy

Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.

For detailed tool chains, output templates, and validation guidance, see references/tool-reference.md.

When to Use

• The user asks about a drug, medication, or therapeutic compound
• The user needs a drug profile, safety assessment, or clinical development overview
• The user requests ADMET evaluation, pharmacogenomics, or regulatory landscape information
• The user provides a compound name, SMILES, or ChEMBL/PubChem identifier for research

Key Principles

1. Report-first approach - The agent creates the report file before any data collection, then populates it progressively.
2. Compound disambiguation first - The agent resolves identifiers (PubChem CID, ChEMBL ID, DailyMed SetID, PharmGKB ID) before beginning research.
3. Citation requirements - Every fact includes inline source attribution with the tool and identifier used.
4. Evidence grading - Claims are graded by evidence strength (T1: Phase 3/FDA label, T2: Phase 1-2/large case series, T3: preclinical, T4: computational).
5. Mandatory completeness - All 11 report sections must exist, even if marked "data unavailable."
6. English-first queries - The agent uses English drug/compound names in tool calls, falling back to original-language terms only if needed. The agent responds in the user's language.

Workflow Overview

Step 1:  Create report file ([DRUG]_drug_report.md) with all 11 section headers
Step 2:  Resolve compound identifiers -> Update Section 1 (Identity)
Step 3:  Retrieve FDA label core fields (mechanism, PK, safety, PGx)
Step 4:  Query PubChem / ADMET-AI / DailyMed -> Update Section 2 (Chemistry)
Step 5:  Query FDA Label MOA + ChEMBL + DGIdb -> Update Section 3 (Mechanism & Targets)
Step 6:  Query ADMET-AI tools (fallback: DailyMed PK) -> Update Section 4 (ADMET)
Step 7:  Query ClinicalTrials.gov -> Update Section 5 (Clinical Development)
Step 8:  Query FAERS / DailyMed -> Update Section 6 (Safety)
Step 9:  Query PharmGKB (fallback: DailyMed PGx) -> Update Section 7 (Pharmacogenomics)
Step 10: Query DailyMed / Orange Book -> Update Section 8 (Regulatory)
Step 11: Query PubMed / literature -> Update Section 9 (Literature)
Step 12: Synthesize findings -> Update Executive Summary & Section 10 (Conclusions)
Step 13: Document all sources, run completeness audit -> Update Section 11

Report Structure

The agent produces an 11-section report in [DRUG]_drug_report.md:

Report Detail Requirements

Each section must be comprehensive and detailed:

• Tables for structured data (targets, trials, adverse events)
• Lists for features, findings, key points
• Paragraphs for narrative synthesis
• Specific values including counts, percentages, and confidence levels (not vague terms)
• Context explaining what the data means, not just what it is
• Source attribution at the end of each data block

Citation Format

The agent attributes every data block to its source:

*Source: PubChem via `PubChem_get_compound_properties_by_CID` (CID: 4091)*

Section-level source summaries appear at the end of each section:

---
**Data Sources for this section:**
- PubChem: `PubChem_get_compound_properties_by_CID` (CID: 4091)
- ChEMBL: `ChEMBL_get_bioactivity_by_chemblid` (CHEMBL1431)
---

Critical Rules

• Avoid ChEMBL_get_molecule_targets — it returns unfiltered, irrelevant results. The agent derives targets from ChEMBL_search_activities instead, filtering to pChEMBL >= 6.0.
• Type normalization — All IDs (ChEMBL, PubMed, NCT) are converted to strings before API calls.
• ADMET fallback — If ADMET-AI tools fail, the agent falls back to FDA label PK sections. Section 4 is never left empty.
• PharmGKB fallback — If PharmGKB is unavailable, the agent uses DailyMed PGx + PubMed literature.
• FAERS limitations — The agent always includes a data limitations paragraph noting voluntary reporting, causality caveats, and reporting bias.
• Clinical trial counts — Section 5.2 shows actual counts by phase/status in table format, not just a list of trials.

Common Use Cases