What is DDI Checker?
A two-stage clinical decision-support system that detects drug-drug interactions using a direct DrugBank dictionary lookup and a Graph Neural Network trained on the pharmacological interaction graph of 4,795 FDA-approved drugs. Built as a graduate-level ML project demonstrating responsible AI in a healthcare context.
DB00682.
Stage 2 — GNN: If nothing is found, a HeteroGraphSAGE network predicts whether an interaction is likely from the pharmacological graph structure and 980 per-drug features.
source label:
documented (DrugBank ground truth),
gnn_predicted (model inference), or
not_found (no documented interaction and GNN confidence below threshold).
GNN predictions always include a safety disclaimer and confidence score.
Users always know whether they are looking at established data or a model prediction.
🗄️ The Largest Drug Database
Every check starts with the full DrugBank database — 4,795 FDA-approved drugs and 824,000 documented interaction pairs, all curated from peer-reviewed literature. Type a drug name, brand name, or synonym and the system resolves it instantly. If an interaction is documented, you get the verbatim clinical description — no interpretation, no black box.
🤖 AI That Reasons Across Pathways
For undocumented pairs, a Graph Neural Network learns from the full interaction network — including shared protein targets, metabolic enzymes, and transporters. When the model flags a potential interaction, it tells you why: which shared pathways drove the prediction. Predictions are always labelled separately from documented data so you know exactly what you're looking at.
📊 91.75 % Accuracy, Peer-Validated
The GNN achieves 0.9738 AUC-ROC on held-out test pairs — far above random chance and above standard logistic regression and rule-based baselines. Every result carries a source label (documented or predicted) and a confidence score, so clinicians and researchers can calibrate how much weight to put on each result.