Tooluniverse Infectious Disease

REASONING STRATEGY — Start Here: Start with pathogen identification: What type of organism? (virus, bacteria, fungus, parasite). Then ask:

• What are the essential proteins? (required for replication or viability — cannot be mutated away)
• Which are surface-exposed? (accessible to drugs and antibodies)
• Which are conserved across strains? (targeting conserved regions prevents resistance escape) These three questions define your drug targets and vaccine candidates. Organisms in the same genus share targets — look up drug precedent for related pathogens before predicting from scratch.

LOOK UP DON'T GUESS: Never assume a pathogen's taxonomy, genome size, or protein function. Always call BVBRC_search_taxonomy or UniProt_search first. Even well-known pathogens have strains with different drug susceptibility profiles — look up the specific strain when known.

When to Use

Apply when user asks:

• "New pathogen detected - what drugs might work?"
• "Emerging virus [X] - therapeutic options?"
• "Drug repurposing candidates for [pathogen]"
• "What do we know about [novel coronavirus/bacteria]?"
• "Essential targets in [pathogen] for drug development"
• "Can we repurpose [drug] against [pathogen]?"

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

1. Create [PATHOGEN]_outbreak_intelligence.md FIRST with section headers
2. Progressively update as data is gathered
3. Output separate files: [PATHOGEN]_drug_candidates.csv, [PATHOGEN]_target_proteins.csv

2. Citation Requirements (MANDATORY)

Every finding must have inline source attribution:

### Target: RNA-dependent RNA polymerase (RdRp)
- **UniProt**: P0DTD1 (NSP12)
- **Essentiality**: Required for replication
*Source: UniProt via `UniProt_search`, literature review*

Phase 0: Tool Verification

Known Parameter Corrections

PubMed tip: Use sort="relevance" (default) not sort="pub_date" — date-sorted queries can return empty for narrow topics. Tool name: PubMed_search_articles. FDA labels: Use FDA_get_drug_label_info_by_field_value with targeted return_fields to avoid oversized responses from OpenFDA_search_drug_labels.

Workflow Overview

Phase 1: Pathogen Identification
├── Taxonomic classification (NCBI Taxonomy)
├── Closest relatives (for knowledge transfer)
├── Genome/proteome availability
└── OUTPUT: Pathogen profile
    |
Phase 2: Target Identification
├── Essential genes/proteins (UniProt)
├── Conservation across strains
├── Druggability assessment (ChEMBL)
└── OUTPUT: Prioritized target list (scored by essentiality/conservation/druggability/precedent)
    |
Phase 3: Structure Prediction (NvidiaNIM)
├── AlphaFold2/ESMFold for targets
├── Binding site identification
├── Quality assessment (pLDDT)
└── OUTPUT: Target structures (docking-ready if pLDDT > 70)
    |
Phase 4: Drug Repurposing Screen
├── Approved drugs for related pathogens (ChEMBL)
├── Broad-spectrum antivirals/antibiotics
├── Docking screen (get_diffdock_info)
└── OUTPUT: Ranked candidate drugs
    |
Phase 4.5: Pathway Analysis
├── KEGG: Pathogen metabolism pathways
├── Essential metabolic targets
├── Host-pathogen interaction pathways
└── OUTPUT: Pathway-based drug targets
    |
Phase 5: Literature Intelligence
├── PubMed: Published outbreak reports
├── BioRxiv/MedRxiv: Recent preprints (CRITICAL for outbreaks)
├── ArXiv: Computational/ML preprints
├── OpenAlex: Citation tracking
├── ClinicalTrials.gov: Active trials
└── OUTPUT: Evidence synthesis
    |
Phase 6: Report Synthesis
├── Top drug candidates with evidence grades
├── Clinical trial opportunities
├── Recommended immediate actions
└── OUTPUT: Final report

Phase Summaries

Phase 1: Pathogen Identification

Classify via NCBI Taxonomy (query param). Identify related pathogens with existing drugs for knowledge transfer. Determine genome/proteome availability.

Knowledge transfer principle: Drugs effective against related pathogens are the highest-priority repurposing candidates. A protease inhibitor for SARS-CoV-1 is immediately relevant to SARS-CoV-2. Look up the related pathogen's approved drugs in ChEMBL before generating candidates from first principles.

Phase 2: Target Identification

Search UniProt for pathogen proteins (reviewed). Check ChEMBL for drug precedent. Score targets by: Essentiality (30%), Conservation (25%), Druggability (25%), Drug precedent (20%). Aim for 5+ targets.

Phase 3: Structure Prediction

Use NvidiaNIM AlphaFold2 for top 3 targets. Assess pLDDT confidence. Only dock structures with pLDDT > 70 (active site > 90 preferred). Fallback: alphafold_get_prediction or ESMFold_predict_structure.

Phase 4: Drug Repurposing Screen

Source candidates from: related pathogen drugs, broad-spectrum antivirals, target class drugs (DGIdb). Dock top 20+ candidates via get_diffdock_info. Rank by docking score and evidence tier.

Phase 4.5: Pathway Analysis

Use KEGG to identify essential metabolic pathways. Map host-pathogen interaction points. Identify pathway-based drug targets beyond direct protein inhibition.

Phase 5: Literature Intelligence

Search PubMed (peer-reviewed), BioRxiv/MedRxiv (preprints - critical for outbreaks), ArXiv (computational), ClinicalTrials.gov (active trials). Track citations via OpenAlex. Note: preprints are NOT peer-reviewed.

Phase 6: Report Synthesis

Aggregate all findings into final report. Grade every candidate. Provide 3+ immediate actions, clinical trial opportunities, and research priorities.

Evidence Grading

Completeness Checklist

Phase 1: Pathogen ID

• Taxonomic classification complete
• Related pathogens identified
• Genome/proteome availability noted

Phase 2: Targets

• 5+ targets identified
• Essentiality documented
• Conservation assessed
• Drug precedent checked

Phase 3: Structures

• Structures predicted for top 3 targets
• pLDDT confidence reported
• Binding sites identified

Phase 4: Drug Screen

• 20+ candidates screened
• FDA-approved drugs prioritized
• Docking scores reported
• Top 5 candidates detailed

Phase 5: Literature

• Recent papers summarized
• Active trials listed
• Resistance data noted

Phase 6: Recommendations

• 3+ immediate actions
• Clinical trial opportunities
• Research priorities

Fallback Chains

References