Drug Interaction Prediction with Graph Machine Learning

Drug response prediction (DRP) remains a critical area of research in cancer treatment. We propose a novel architecture for pan-cancer drug response prediction that leverages graph-based representations of both cellular and pharmaceutical data. Our approach transforms protein-protein interaction networks to capture unique cancer mutations in each cell line, while simultaneously representing drugs through their molecular structures. Unlike traditional DRP models, our dual-graph approach enables us to exploit the inherent structural relationships in both cell line and drug data. By applying graph machine learning algorithms to these representations, we develop a robust model that predicts drug-specific half-maximal inhibitory concentration dosages for individual cell lines.