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Phase 1: Pathogen Identification
├── Taxonomic classification
├── Closest relatives (for knowledge transfer)
├── Genome/proteome availability
└── OUTPUT: Pathogen profile
    ↓
Phase 2: Target Identification
├── Essential genes/proteins
├── Conserved across strains
├── Druggability assessment
└── OUTPUT: Prioritized target list
    ↓
Phase 3: Structure Prediction (NvidiaNIM)
├── AlphaFold2/ESMFold for targets
├── Binding site identification
├── Quality assessment (pLDDT)
└── OUTPUT: Target structures
    ↓
Phase 4: Drug Repurposing Screen
├── Approved drugs for related pathogens
├── Broad-spectrum antivirals/antibiotics
├── Docking screen (NvidiaNIM_diffdock)
└── OUTPUT: Candidate drugs
    ↓
Phase 4.5: Pathway Analysis (NEW)
├── KEGG: Pathogen metabolism pathways
├── Essential metabolic targets
├── Host-pathogen interaction pathways
└── OUTPUT: Pathway-based drug targets
    ↓
Phase 5: Literature Intelligence (ENHANCED)
├── PubMed: Published outbreak reports
├── BioRxiv/MedRxiv: Recent preprints (CRITICAL for outbreaks)
├── ArXiv: Computational/ML preprints
├── OpenAlex: Citation tracking
└── OUTPUT: Evidence synthesis
    ↓
Phase 6: Report Synthesis
├── Top drug candidates
├── Clinical trial opportunities
├── Recommended immediate actions
└── OUTPUT: Final report

def identify_pathogen(tu, pathogen_query):
    """Classify pathogen taxonomically."""
    
    # NCBI Taxonomy search
    taxonomy = tu.tools.NCBI_Taxonomy_search(query=pathogen_query)
    
    return {
        'taxid': taxonomy.get('taxid'),
        'scientific_name': taxonomy.get('scientific_name'),
        'rank': taxonomy.get('rank'),
        'lineage': taxonomy.get('lineage'),
        'type': classify_type(taxonomy)  # virus, bacteria, fungus, parasite
    }

def find_related_pathogens(tu, taxid):
    """Find related pathogens for drug knowledge transfer."""
    
    # Get family/genus level relatives
    relatives = tu.tools.NCBI_Taxonomy_get_children(
        taxid=taxid,
        rank="genus"
    )
    
    # Find relatives with approved drugs
    related_with_drugs = []
    for rel in relatives:
        drugs = tu.tools.ChEMBL_search_targets(
            query=rel['scientific_name'],
            organism_contains=True
        )
        if drugs:
            related_with_drugs.append({
                'pathogen': rel,
                'drugs': drugs
            })
    
    return related_with_drugs

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def identify_targets(tu, pathogen_name):
    """Identify essential druggable targets."""
    
    # Search UniProt for pathogen proteins
    proteins = tu.tools.UniProt_search(
        query=f"organism:{pathogen_name}",
        reviewed=True
    )
    
    # Prioritize by essentiality and druggability
    targets = []
    for protein in proteins:
        # Check for known drug interactions
        chembl_target = tu.tools.ChEMBL_search_targets(
            query=protein['gene_name']
        )
        
        targets.append({
            'uniprot': protein['accession'],
            'name': protein['protein_name'],
            'function': protein['function'],
            'has_drug_precedent': len(chembl_target) > 0,
            'druggability': assess_druggability(protein)
        })
    
    return rank_targets(targets)

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def predict_target_structure(tu, sequence, target_name):
    """Predict structure for target protein."""
    
    # Use AlphaFold2 for high accuracy
    structure = tu.tools.NvidiaNIM_alphafold2(
        sequence=sequence,
        algorithm="mmseqs2",
        relax_prediction=False
    )
    
    # Parse pLDDT confidence
    plddt_scores = parse_plddt(structure)
    
    return {
        'structure': structure['structure'],
        'mean_plddt': np.mean(plddt_scores),
        'high_confidence_regions': get_high_confidence(plddt_scores),
        'predicted_binding_site': identify_binding_site(structure)
    }

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def get_repurposing_candidates(tu, target_name, pathogen_family):
    """Find approved drugs to repurpose."""
    
    candidates = []
    
    # 1. Drugs approved for related pathogens
    related_drugs = tu.tools.ChEMBL_search_drugs(
        query=pathogen_family,
        max_phase=4
    )
    candidates.extend(related_drugs)
    
    # 2. Broad-spectrum antivirals
    antivirals = tu.tools.ChEMBL_search_drugs(
        query="broad spectrum antiviral",
        max_phase=4
    )
    candidates.extend(antivirals)
    
    # 3. Drugs with known activity against target class
    target_class_drugs = tu.tools.DGIdb_get_drug_gene_interactions(
        genes=[target_name]
    )
    candidates.extend(target_class_drugs)
    
    return deduplicate(candidates)

def dock_candidates(tu, target_structure, candidate_smiles_list):
    """Dock candidate drugs against target."""
    
    results = []
    for smiles in candidate_smiles_list:
        docking = tu.tools.NvidiaNIM_diffdock(
            protein=target_structure,
            ligand=smiles,
            num_poses=5
        )
        
        results.append({
            'smiles': smiles,
            'top_score': docking['poses'][0]['confidence'],
            'poses': docking['poses']
        })
    
    return sorted(results, key=lambda x: x['top_score'], reverse=True)

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def analyze_pathogen_pathways(tu, pathogen_name, pathogen_type):
    """Identify druggable metabolic pathways in pathogen."""
    
    # KEGG pathogen pathways
    pathways = tu.tools.kegg_search_pathway(
        query=f"{pathogen_name} metabolism"
    )
    
    # Essential metabolic genes
    essential_genes = tu.tools.kegg_get_pathway_genes(
        pathway_id=pathways[0]['pathway_id']
    )
    
    # Host-pathogen interaction pathways
    host_pathogen = tu.tools.kegg_search_pathway(
        query=f"{pathogen_name} host interaction"
    )
    
    return {
        'metabolic_pathways': pathways,
        'essential_genes': essential_genes,
        'host_interaction': host_pathogen
    }

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def comprehensive_outbreak_literature(tu, pathogen_name):
    """Search all literature sources for outbreak intelligence."""
    
    # PubMed: Peer-reviewed
    pubmed = tu.tools.PubMed_search_articles(
        query=f"{pathogen_name} AND (outbreak OR treatment OR drug)",
        limit=50,
        sort="date"
    )
    
    # BioRxiv: CRITICAL for outbreaks - newest findings
    biorxiv = tu.tools.BioRxiv_search_preprints(
        query=f"{pathogen_name} treatment mechanism",
        limit=20
    )
    
    # MedRxiv: Clinical preprints
    medrxiv = tu.tools.MedRxiv_search_preprints(
        query=f"{pathogen_name} clinical trial",
        limit=20
    )
    
    # ArXiv: Computational/ML papers
    arxiv = tu.tools.ArXiv_search_papers(
        query=f"{pathogen_name} drug discovery",
        category="q-bio",
        limit=10
    )
    
    # Clinical trials
    trials = tu.tools.search_clinical_trials(
        condition=pathogen_name,
        status="Recruiting"
    )
    
    # Citation analysis
    key_papers = pubmed[:10]
    for paper in key_papers:
        citation = tu.tools.openalex_search_works(
            query=paper['title'],
            limit=1
        )
        paper['citations'] = citation[0].get('cited_by_count', 0) if citation else 0
    
    return {
        'pubmed': pubmed,
        'biorxiv': biorxiv,
        'medrxiv': medrxiv,
        'arxiv': arxiv,
        'trials': trials,
        'key_papers': key_papers
    }

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