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.
IMPORTANT
Always use English terms in tool calls (drug names, disease names, target names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
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
Ask about disease module coverage by a compound's targets
NOT for
(use other skills instead):
Simple drug repurposing without network analysis -> Use
tooluniverse-drug-repurposing
Single target validation -> Use
tooluniverse-drug-target-validation
Adverse event detection only -> Use
tooluniverse-adverse-event-detection
General disease research -> Use
tooluniverse-disease-research
GWAS interpretation -> Use
tooluniverse-gwas-snp-interpretation
Input Parameters
Parameter
Required
Description
Example
entity
Yes
Compound name/ID, target gene symbol/ID, or disease name/ID
metformin
,
EGFR
,
Alzheimer disease
entity_type
No
Type hint:
compound
,
target
, or
disease
(auto-detected if omitted)
compound
analysis_mode
No
compound-to-disease
,
disease-to-compound
,
target-centric
,
bidirectional
(default)
bidirectional
secondary_entity
No
Second entity for focused analysis (e.g., disease for compound input)
Alzheimer disease
Network Pharmacology Score (0-100)
Component
Max Points
Criteria for Max
Network Proximity
35
Z < -2, p < 0.01
Clinical Evidence
25
Approved for related indication
Target-Disease Association
20
Strong genetic evidence (GWAS, rare variants)
Safety Profile
10
FDA-approved, favorable safety
Mechanism Plausibility
10
Clear pathway mechanism with functional evidence
Priority Tiers
Score
Tier
Recommendation
80-100
Tier 1
High repurposing potential - proceed with experimental validation
60-79
Tier 2
Good potential - needs mechanistic validation
40-59
Tier 3
Moderate potential - high-risk/high-reward
0-39
Tier 4
Low potential - consider alternative approaches
Evidence Grading
Tier
Criteria
Examples
T1
Human clinical proof, regulatory evidence
FDA-approved, Phase III trial
T2
Functional experimental evidence
IC50 < 1 uM, CRISPR screen
T3
Association/computational evidence
GWAS hit, network proximity
T4
Prediction, annotation, text-mining
AlphaFold, literature co-mention
Full scoring details:
SCORING_REFERENCE.md
Key Principles
Report-first approach
- Create report file FIRST, then populate progressively
Entity disambiguation FIRST
- Resolve all identifiers before analysis
Bidirectional network
- Construct C-T-D network comprehensively from both directions