GraphFusion: Integrative prediction of drug synergy using multi-scale graph representations and cell line contexts.

Abstract

Predicting the synergy of drug combinations is crucial for cancer treatment and drug development. Accurate prediction requires the integration of multiple types of data, including molecular structures of individual drugs, available synergy scores between drugs, and gene expression information from different cancer cell lines. The first two types contain multi-scale information within or between drugs, while the cell lines serve as the contextual background for drug interactions. Existing machine learning methods fail to fully utilize and integrate these information, leading to suboptimal performance. To address this issue, we introduce GraphFusion, an innovative approach that combines molecular graphs and drug synergy graphs with cell line contextual information. By employing novel GCN and Graphormer modules capable of accepting and utilizing external information, GraphFusion integrates these two levels of graph information. Specifically, the molecular graphs pass fine-grained structural information to the synergy graphs, while the synergy graphs convey global drug interaction data to the molecular graphs. Additionally, cell line information is incorporated as contextual background. This comprehensive integration enables GraphFusion to achieve state-of-the-art results on the O'Neil and NCI-ALMANAC datasets.