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<p>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&#xa0;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.</p>

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Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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  • Yanan Tian,
  • Ruiqiang Lu,
  • Xiaoqing Gong,
  • Wei Zhao,
  • Yuquan Li,
  • Xiaorui Wang,
  • Xinming Jia,
  • Qin Li,
  • Yuwei Yang,
  • Henry H. Y. Tong,
  • Joel P. Arrais,
  • Xiaojun Yao,
  • Huanxiang Liu

摘要

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.