PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction

作者：Xinwei Zhao, Junqing Xu, Youyuan Shui, Mengdie Xu, Jie Hu, Xiaoyan Liu, Kai Che, Junjie Wang, Yun Liu · 发表于：Journal of Cheminformatics · 年份：2024 · DOI：10.1186/s13321-024-00839-8 · 被引用次数：20 · 研究领域：Computational Drug Discovery Methods、Cell Image Analysis Techniques、Gene expression and cancer classification

MOTIVATION: Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. RESULTS: In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . SCIENTIFIC CONTRIBUTION: First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations....