Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.

ABSTRACT: Protein-ligand binding affinity assessment plays a pivotal role in virtual drug screening, yet conventional data-driven approaches rely heavily on limited protein-ligand crystal structures. Structure-free compound-protein interaction (CPI) methods have emerged as competitive alternatives, leveraging extensive bioactivity data to serve as more robust scoring functions. However, these methods often overlook two critical challenges that affect data efficiency and modeling accuracy: the heterogeneity of bioactivity data due to differences in bioassay measurements and the presence of activity cliffs (ACs)─small chemical modifications that lead to significant changes in bioactivity, which have not been thoroughly investigated in CPI modeling. To address these challenges, we present CPI2M, a larg

SUBMITTER: Gu Y 

PROVIDER: S-EPMC12529763 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature