Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations.

ABSTRACT: Compound-protein interaction (CPI) prediction plays a crucial role in drug discovery by aiding the identification of binding and affinities between small molecules and proteins. Current deep learning models rely heavily on sequence-based representations and suffer from a lack of labeled data, which restricts their accuracy and generalizability. To overcome these challenges, we propose GenSPARC (a model with Generalized Structure- and Property-Aware Representations of protein and chemical language models for CPI prediction), a deep learning model that leverages structure-aware protein representations derived from AlphaFold2 predictions and FoldSeek's three-dimensional interaction alphabet. Compound features were extracted using graph convolutional networks and a pretrained chemical language

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12823658 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature