Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
Computer Science
Thesis Sponsor/Dissertation Chair/Project Chair
Hao Liu
Committee Member
Aparna S. Varde
Committee Member
Raina Samuel
Abstract
Pharmaceutical treatments are essential for managing medical conditions, but drug-drug interactions (DDIs) pose significant risks to patient safety and healthcare outcomes. This research integrates Knowledge Graphs and Graph Neural Networks to predict DDIs by exploring complex drug relationships. We construct a comprehensive knowledge graph using DrugBank data (1,000 drugs, 155,774 interactions) enriched with molecular features from PubChem. Our methodology introduces a novel multi-modal approach by integrating transformer-based embeddings (ChemBERTa, SPECTER, and SBERT) to create 1152-dimensional feature vectors that capture structural, biomedical literature, and semantic properties of drugs. Formulating DDI prediction as a link prediction task, we compare three Graph Neural Network architectures: Graph Convolutional Network (GCN), GraphSAGE, and Graph Attention Network (GAT). With basic molecular features, GCN achieved 75.65% accuracy (80.17% F1 score). After multi-modal integration, performance improved significantly across all models, with GAT showing the greatest enhancement (80.61% accuracy, 82.57% F1 score). Case studies with documented drug interactions demonstrate the models' clinical relevance, particularly for cardiovascular and antimicrobial medications. This work demonstrates the importance of integrating diverse data modalities for DDI prediction and has important implications for enhancing medication safety in polypharmacy scenarios, especially for elderly patients with multiple chronic conditions.
File Format
Recommended Citation
Chianumba, Ernest C., "Advancing Drug-Drug Interaction Prediction Using Multi-Modal Feature Integration with Graph Neural Networks" (2025). Theses, Dissertations and Culminating Projects. 1546.
https://digitalcommons.montclair.edu/etd/1546