Tooluniverse Network Pharmacology

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Network Pharmacology Pipeline

Construct and analyze compound-target-disease (C-T-D) networks to identify drug repurposing opportunities, understand polypharmacology, and predict drug mechanisms using systems pharmacology approaches.

LOOK UP DON'T GUESS - Retrieve actual target lists, network data, and clinical evidence from tools. Do not infer network relationships from drug class alone.

IMPORTANT: Always use English terms in tool calls, even if the user writes in another language. Respond in the user's language.

Polypharmacology Reasoning (Start Here)

Before building any network, reason about what kind of multi-target effect you are dealing with:

A drug hitting multiple targets is either polypharmacology (desired multi-target) or promiscuity (undesired off-target). The distinction depends on whether the additional targets contribute to efficacy or cause toxicity.

Use this framework to guide the analysis:

• Desired polypharmacology: multiple targets all lie within the same disease module or pathway. Example: a kinase inhibitor that hits both EGFR and ERBB2 in the same signaling cascade. Look for pathway co-membership and disease module overlap. This is a network proximity argument.
• Off-target promiscuity: additional targets are in unrelated pathways, especially those associated with known toxicity (hERG for cardiotoxicity, CYP3A4 for drug interactions, COX-1 for GI toxicity). Look for these in the safety phase before claiming benefit.
• Repurposing hypothesis: the drug's known targets have strong genetic/functional evidence for the new disease. Network proximity (Z-score) quantifies this. A Z < -2 with p < 0.01 is meaningful signal; a Z near 0 means the targets are essentially unconnected to the disease module.
• Mechanism ambiguity: if a drug has 10+ known targets, do not treat all as therapeutically relevant. Start with primary mechanism-of-action targets, then ask whether secondary targets add to or subtract from the therapeutic window.

Document this reasoning explicitly in the report before listing candidates.

When to Use This Skill

Apply when users:

• Ask "Can [drug] be repurposed for [disease] based on network analysis?"
• Want to understand multi-target (polypharmacology) effects of a compound
• Need compound-target-disease network construction and analysis
• Ask about network proximity between drug targets and disease genes
• Want systems pharmacology analysis of a drug or target
• Ask about drug repurposing candidates ranked by network metrics
• Need mechanism prediction for a drug in a new indication
• Want to identify hub genes in disease networks as therapeutic targets

NOT for (use other skills instead):

• Simple drug repurposing without network analysis -> tooluniverse-drug-repurposing
• Single target validation -> tooluniverse-drug-target-validation
• Adverse event detection only -> tooluniverse-adverse-event-detection

Key Principles

1. Report-first approach - Create report file FIRST, then populate progressively
2. Entity disambiguation FIRST - Resolve all identifiers before analysis
3. Reason about polypharmacology type - Desired vs. promiscuous (see above)
4. Bidirectional network - Construct C-T-D network from both directions
5. Rank candidates - Prioritize by composite Network Pharmacology Score
6. Mechanism prediction - Explain HOW drug could work via network paths
7. Clinical feasibility - FDA-approved drugs ranked higher than preclinical
8. Safety context - Flag known adverse events and off-target liabilities
9. Evidence grading - Grade all evidence T1-T4
10. Negative results documented - "No data" is data; empty sections are failures
11. Source references - Every finding must cite the source tool/database

Network Pharmacology Score (0-100)

Five components with explicit reasoning at each step:

• Network Proximity (35 pts): Z < -2, p < 0.01 earns full points. A drug whose targets are in a different network neighborhood from the disease module scores near zero here. Do not claim proximity without computing the Z-score.
• Clinical Evidence (25 pts): Approved for related indication earns full points. Clinical trial evidence earns partial credit. Computational prediction alone earns none.
• Target-Disease Association (20 pts): Strong genetic evidence (GWAS, rare variants) for the drug's primary targets in the new disease.
• Safety Profile (10 pts): FDA-approved, favorable safety in target population.
• Mechanism Plausibility (10 pts): A clear pathway mechanism with functional evidence, not just co-mention in literature.

Priority tiers: 80-100 = high repurposing potential (proceed to experimental validation); 60-79 = good potential (needs mechanistic validation); 40-59 = moderate potential (high-risk/high-reward); 0-39 = low potential.

Evidence grades: T1 = human clinical proof; T2 = functional experimental evidence (IC50 < 1 uM, CRISPR screen); T3 = association/computational (GWAS hit, network proximity); T4 = prediction or text-mining only.

Full scoring details: SCORING_REFERENCE.md

Workflow Overview

Phase 0: Entity Disambiguation and Report Setup

• Create report file immediately
• Resolve entity to all required IDs (ChEMBL, DrugBank, PubChem CID, Ensembl, MONDO/EFO)
• Tools: OpenTargets_get_drug_chembId_by_generic_name, drugbank_get_drug_basic_info_by_drug_name_or_id, PubChem_get_CID_by_compound_name, OpenTargets_get_target_id_description_by_name, OpenTargets_get_disease_id_description_by_name

Phase 1: Network Node Identification

• Compound nodes: Drug targets, mechanism of action, current indications
• Target nodes: Disease-associated genes, GWAS targets, druggability levels
• Disease nodes: Related diseases, hierarchy, phenotypes
• Tools: OpenTargets_get_drug_mechanisms_of_action_by_chemblId, OpenTargets_get_associated_targets_by_drug_chemblId, drugbank_get_targets_by_drug_name_or_drugbank_id, DGIdb_get_drug_gene_interactions, CTD_get_chemical_gene_interactions, OpenTargets_get_associated_targets_by_disease_efoId, Pharos_get_target

Phase 2: Network Edge Construction

• C-T edges: Bioactivity data (ChEMBL, DrugBank, BindingDB)
• T-D edges: Genetic/functional associations (OpenTargets evidence, GWAS, CTD)
• C-D edges: Clinical trials, CTD chemical-disease, literature co-mentions
• T-T edges: PPI network (STRING, IntAct, OpenTargets interactions, HumanBase)
• Tools: ChEMBL_get_target_activities, OpenTargets_target_disease_evidence, GWAS_search_associations_by_gene, search_clinical_trials, CTD_get_chemical_diseases, STRING_get_interaction_partners, STRING_get_network, intact_search_interactions, humanbase_ppi_analysis

Phase 3: Network Analysis

• Hub identification: which targets are most connected in the drug-disease subnetwork
• Shortest paths between drug targets and disease genes: how many hops, through which intermediaries
• Network proximity Z-score: are drug targets closer to disease module than random expectation
• Use the `Network_proximity` tool — Guney/Barabasi (2016) + Menche (2015) set-distance with a degree-matched Z-score, computed deterministically from a graph you supply (inline edges or an edgelist_path) plus two node sets (set_a/set_b, or the aliases targets/disease_genes). measure = closest (default), shortest, or separation (s_AB < 0 ⇒ overlapping modules). Returns value, z_score, p_value.