
| Title of the Project |
MedGuard AI |
| Students Details |
202211182 Salma Roshdy 202110170 Maysoon Adil 202110784 Raghad Tarkhan 202111554 Sabrin Ibrahim |
| Abstract |
MedGuard is an AI-powered clinical decision support system for predicting drug–drug and drug–food interaction risks. Unlike traditional lookup tools that mainly detect
documented interactions, MedGuard uses molecular deep learning to estimate potential side effects from drug structure and patient medication context. The system combines
GNN-based molecular modeling, ChemBERTa-2 embeddings, Morgan fingerprints, and a HeteroGNN drug–food module inside one role-based platform for patients, doctors,
and pharmacists.
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