MulinforCPI: enhancing precision of compound-protein interaction prediction through novel perspectives on multi-level information integration.

ABSTRACT: Forecasting the interaction between compounds and proteins is crucial for discovering new drugs. However, previous sequence-based studies have not utilized three-dimensional (3D) information on compounds and proteins, such as atom coordinates and distance matrices, to predict binding affinity. Furthermore, numerous widely adopted computational techniques have relied on sequences of amino acid characters for protein representations. This approach may constrain the model's ability to capture meaningful biochemical features, impeding a more comprehensive understanding of the underlying proteins. Here, we propose a two-step deep learning strategy named MulinforCPI that incorporates transfer learning techniques with multi-level resolution features to overcome these limitations. Our approach lev

SUBMITTER: Nguyen NQ 

PROVIDER: S-EPMC10768804 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature