Synergistic Drug Combination Prediction via Dual-Level Feature Aggregation and Knowledge Graph-Based Deep Neural Network

Identifying synergistic drug combinations is a critical but difficult challenge in cancer treatment, owing to the sheer complexity and enormous number of possible drug combinations. However, most existing computational methods rely on a single data perspective and often overlooking the complexity of interactions between different biological entities. Furthermore, they fail to fully integrate the intrinsic properties of drugs and cell lines with the broader biological relationships that play a crucial role in drug synergy. To address these challenges, we propose a novel framework called LGSyn that integrates two types of information: local features, including molecular fingerprints, descriptors, and gene expression profiles, as well as global features that encompass broader biological interactions, including drug-protein, protein-cell line, protein-protein, and cell line-tissue interactions. By combining these two types of features, LGSyn leverages the full spectrum of biological knowledge to predict drug synergy. In LGSyn, we developed three fusion strategies to effectively integrate local and global information and identify the most suitable strategy. The resulting fused feature vectors are then fed into a deep neural network for training and synergy prediction. Experimental results demonstrate that the proposed method outperforms current state-of-the-art models, achieving superior accuracy and stability in drug synergy prediction.