Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.

Abstract

Developing selective kinase inhibitors is challenging due to the conserved kinase structures and costly kinome profiling experiments, highlighting the need for accurate prediction of kinase-inhibitor affinity and specificity. Here we present MMCLKin, an attention consistency-guided contrastive learning framework that integrates geometric graph and sequence networks with multi-head attention and multimodal, multiscale contrastive learning to accurately and interpretably predict kinase-inhibitor activity and selectivity. MMCLKin outperforms existing methods across two 3D kinase-drug datasets and demonstrates strong generalizability on ten diverse protein-drug and one mutation-aware datasets, and effectively screens on both known and unknown kinase structures. In-depth analysis of attention coefficients reveals that MMCLKin can identify key residues and molecular functional groups critical for kinase-inhibitor binding. Additionally, ADP-Glo assays confirm that five out of 20 MMCLKin-identified compounds inhibit the pathogenic LRRK2 G2019S mutant, with four exhibiting nanomolar-level potency. Collectively, MMCLKin represents a useful tool for discovering potent and selective kinase inhibitors.