Predicting drug-target binding affinity with cross-scale graph contrastive learning.

ABSTRACT: Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experim

SUBMITTER: Wang J 

PROVIDER: S-EPMC10788681 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature