Drug discovery of synergistic combinations via multilayer deep learning models:Advances and challenges

Although combination drug therapies hold great promise for complex diseases, their development is hindered by the complexity of biological networks and the combinatorial explosion of possible drug interactions. Deep learning (DL) models offer a transformative solution by integrating multimodal data and biomedical networks to predict drug combination synergy with high accuracy. These models automatically extract complex patterns from high-dimensional data, overcoming limitations of conventional methods, accelerating rational combination discovery. Here, we systematically examined diverse network-based DL frameworks, analyzing how increasing structural complexity enhances prediction performance while maintaining interpretability. While current methodologies show encouraging results, challenges remain in data quality, model generalization, and clinical translation. Here, we highlight pivotal studies demonstrating in different DL models’ potential, outlines their key limitations, and discusses future directions including multimodal learning and mechanistic interpretability, to establish multilayer DL model as a cornerstone of next-generation drug combination discovery.