tooluniverse-network-pharmacology

What it does

Construct and analyze compound-target-disease networks to identify drug repurposing opportunities, understand polypharmacology, and predict drug mechanisms using systems pharmacology approaches. Builds multi-layer networks from multiple data sources and computes a Network Pharmacology Score (0-100) to rank candidates.

How it works

• Phase 0: Entity Disambiguation and Report Setup
  • Create a report file immediately and resolve input entities (compound, target, disease) to identifiers and cross-references using diverse tools.
• Phase 1: Network Node Identification
  • Identify compound nodes via drug MoA and associated targets from OpenTargets, DrugBank targets, DGIdb interactions, CTD chemical-gene interactions, STITCH interactions, and current indications and approvals.
  • Identify target nodes by retrieving disease-associated targets, gathering evidence for targets, GWAS studies, CTD gene-disease links, and Pharos target development levels.
  • Identify disease nodes by finding related diseases, disease hierarchy, phenotypes, and therapeutic areas.
• Phase 2: Network Edge Construction
  • 2.1 Compound-target edges: gather bioactivity data (ChEMBL), mechanisms, DrugBank targets and pharmacology, and binding data if available.
  • 2.2 Target-disease edges: retrieve OpenTargets target-disease evidence, GWAS associations, CTD gene-disease links, and PharmGKB gene details.
  • 2.3 Compound-disease edges: collect clinical trial evidence, CTD chemical-disease associations, PubMed and Europe PMC literature evidence.
  • 2.4 Target-target edges: obtain STRING protein-protein interactions and related network edges.
• Phase 3: Scoring and Ranking (implied by inputs)
  • Calculate Network Proximity, Clinical Evidence, Target-Disease Association strength, Safety Profile, and Mechanism Plausibility to form a composite Network Pharmacology Score (0-100).
  • Rank candidates by the score and provide mechanism predictions and hub analyses.
• Phase 4: Reporting and References
  • Apply the report-first principle, cite every finding to source tools/databases, and include an evidence grading framework (T1-T4).

When to use it

Use when users ask about drug repurposing via network analysis, multi-target drug effects, compound-target-disease networks, systems pharmacology, or polypharmacology. Not for simple, non-network-based repurposing, single-target validation, or purely adverse-event analysis.

What it can touch

• Tools referenced in the workflow include OpenTargets, ChEMBL, DrugBank, DGIdb, CTD, STITCH, PubMed, Europe PMC, STRING, PharmGKB, Pharos, and GWAS resources. The exact tool calls and data endpoints are listed within the workflow steps (e.g., OpenTargets_get_drug_chembId_by_generic_name, ChEMBL_get_target_activities, STRING_get_interaction_partners, etc.).

Caveats

• License: NOASSERTION.
• The workflow relies on multiple external data sources; data availability and update frequency may affect results. The skill emphasizes using English terms in tool calls for names and diseases and requires entity disambiguation and citation of evidence.