| name | tooluniverse-network-pharmacology |
| description | Compound-target-disease network construction and analysis for drug repurposing, polypharmacology discovery, and multi-target drug design. Uses STRING, BioGRID, ChEMBL, DGIdb, OMIM, OpenTargets. Use for off-target effect prediction, network-based drug repurposing, and identifying molecules with desired multi-target profile. |
| disable-model-invocation | true |
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.
