Improving compound-protein interaction prediction by focusing on intra-modality and inter-modality dynamics with a multimodal tensor fusion strategy.

ABSTRACT: Identifying novel compound-protein interactions (CPIs) plays a pivotal role in target identification and drug discovery. Although the recent multimodal methods have achieved outstanding advances in CPI prediction, they fail to effectively learn both intra-modality and inter-modality dynamics, which limits their prediction performance. To address the limitation, we propose a novel multimodal tensor fusion CPI prediction framework, named MMTF-CPI, which contains three unimodal learning modules for structure, heterogeneous network and transcriptional profiling modalities, a tensor fusion module and a prediction module. MMTF-CPI is capable of focusing on both intra-modality and inter-modality dynamics with the tensor fusion module. We demonstrated that MMTF-CPI is superior to multiple state-of

SUBMITTER: Wang M 

PROVIDER: S-EPMC11544084 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature