Improving drug-target affinity prediction by adaptive self-supervised learning.

ABSTRACT: Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction. The first difficulty lies in the phenomenon of sample mismatch: self-supervised learning processes drug and target samples independently, while actual prediction requires the integration of drug-target pairs. Another challenge is the mismatch between the broadness of self-supervised learning objectives and the precision of biological mechanisms of drug-target affinity (i.e., the induced-fit principle). The former focuses on global feature extraction, while the latter emphasizes the importance of local precise matching. To address these issues, an adaptive self-supervised learning-based d

SUBMITTER: Ye Q 

PROVIDER: S-EPMC11784864 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature