Drug-Drug Interaction Discovery
Explainable graph ML workflow for biomedical interaction review
GNN over a biomedical knowledge graph to flag drug-drug interactions. Claude API turns ranked interaction scores into human-readable output.
Visual evidence
System visualization
Case study
Problem, Build, Outcome
Problem
Drug interaction review is hard to scale because useful signals are spread across biomedical entities, relationships, and literature-style context.
Build
Modeled drugs and biological relationships as a knowledge graph, then used a GNN ranking flow with Claude-generated summaries for explainable results.
Outcome
A reviewer can move from interaction candidates to a readable rationale without manually traversing the graph.
Architecture
System Flow
Architecture Notes
- Graph layer organizes drugs, biological concepts, and relationship edges.
- Model layer ranks candidate interactions from graph structure and learned signals.
- Explanation layer turns ranked candidates into review-ready natural language.
Engineering choices
Technical Decisions
Represented drugs and related biomedical entities as graph nodes so relationships could be scored instead of flattened into isolated features.
Used a GNN-style ranking flow to keep the model focused on interaction candidates and relative risk ordering.
Added Claude-generated summaries so scored candidates could be reviewed as readable evidence instead of opaque model output.
Evidence
Proof Points
Biomedical knowledge graph representation
Ranked interaction candidates
LLM-generated explanations for review
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