drug-discovery-prompts/upstream/mims-harvard-ToolUniverse/skills/tooluniverse-infectious-disease/phase_details.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Infectious Disease Outbreak Intelligence - Phase Details		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-infectious-disease/phase_details.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Infectious Disease Outbreak Intelligence - Phase Details

Phase 1: Pathogen Identification

1.1 Taxonomic Classification

def identify_pathogen(tu, pathogen_query):
    """Classify pathogen taxonomically."""
    taxonomy = tu.tools.NCBIDatasets_suggest_taxonomy(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
    }

1.2 Related Pathogens (Knowledge Transfer)

def find_related_pathogens(tu, taxid):
    """Find related pathogens for drug knowledge transfer."""
    relatives = tu.tools.NCBI_Taxonomy_get_children(taxid=taxid, rank="genus")
    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

1.3 Output Example

## 1. Pathogen Profile

### 1.1 Taxonomic Classification

| Property | Value |
|----------|-------|
| **Organism** | SARS-CoV-2 |
| **Taxonomy ID** | 2697049 |
| **Type** | RNA virus (positive-sense, single-stranded) |
| **Family** | Coronaviridae |
| **Genus** | Betacoronavirus |

### 1.2 Related Pathogens with Drug Precedent

| Relative | Similarity | Approved Drugs | Relevance |
|----------|------------|----------------|-----------|
| SARS-CoV | 79% genome | Remdesivir (EUA) | High |
| MERS-CoV | 50% genome | None approved | Medium |

---

Phase 2: Target Identification

2.1 Essential Protein Identification

def identify_targets(tu, pathogen_name):
    """Identify essential druggable targets."""
    proteins = tu.tools.UniProt_search(
        query=f"organism:{pathogen_name}", reviewed=True
    )
    targets = []
    for protein in proteins:
        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)

2.2 Target Prioritization Criteria

Criterion	Weight	Description
Essentiality	30%	Required for replication/survival
Conservation	25%	Conserved across strains/variants
Druggability	25%	Structural features amenable to binding
Drug precedent	20%	Existing drugs for homologous targets

---

Phase 3: Structure Prediction

def predict_target_structure(tu, sequence, target_name):
    """Predict structure using AlphaFold2 via NVIDIA NIM."""
    structure = tu.tools.NvidiaNIM_alphafold2(
        sequence=sequence, algorithm="mmseqs2", relax_prediction=False
    )
    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)
    }

pLDDT Quality Assessment

pLDDT Range	Confidence	Use for Docking
>90	Very High	Excellent
70-90	High	Good
50-70	Medium	Use caution
<50	Low	Not recommended

---

Phase 4: Drug Repurposing Screen

4.1 Identify Candidates

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

4.2 Docking Screen (NVIDIA NIM)

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)

---

Phase 4.5: Pathway Analysis

def analyze_pathogen_pathways(tu, pathogen_name, pathogen_type):
    """Identify druggable metabolic pathways in pathogen."""
    pathways = tu.tools.kegg_search_pathway(query=f"{pathogen_name} metabolism")
    essential_genes = tu.tools.KEGG_get_pathway_genes(
        pathway_id=pathways[0]['pathway_id'])
    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
    }

---

Phase 5: Literature Intelligence

def comprehensive_outbreak_literature(tu, pathogen_name):
    """Search all literature sources for outbreak intelligence."""
    pubmed = tu.tools.PubMed_search_articles(
        query=f"{pathogen_name} AND (outbreak OR treatment OR drug)",
        limit=50, sort="date")
    biorxiv = tu.tools.BioRxiv_list_recent_preprints(
        query=f"{pathogen_name} treatment mechanism", limit=20)
    medrxiv = tu.tools.MedRxiv_get_preprint(
        query=f"{pathogen_name} clinical trial", limit=20)
    arxiv = tu.tools.ArXiv_search_papers(
        query=f"{pathogen_name} drug discovery", category="q-bio", limit=10)
    trials = tu.tools.search_clinical_trials(
        condition=pathogen_name, status="Recruiting")

    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
    }

Note: Preprints (BioRxiv/MedRxiv) are NOT peer-reviewed but CRITICAL for outbreak intelligence. Always note this caveat in reports.