1136-Deep Learning Architectures for Drug Synergy Prediction

Paper Talk · Jun 16, 2026 · 25m

This review explores the evolution and application of deep learning for predicting anti-cancer drug synergy, a therapeutic approach that improves efficacy while minimizing side effects. It categorizes computational models into single-task and multi-task learning frameworks, detailing how they utilize biomedical data such as chemical structures and genomic profiles. The authors highlight various benchmarking datasets and interactive tools while comparing different architectural strategies, including branch and graph structures. Despite significant progress, the review identifies critical…