Shimazaki Tomomi, Tachikawa Masanori
Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Yokohama, Kanagawa 236-0027, Japan.
Graduate School of Data Science, Yokohama City University, 22-2, Seto, Yokohama, Kanagawa 236-0027, Japan.
ACS Omega. 2022 Mar 18;7(12):10372-10381. doi: 10.1021/acsomega.1c06976. eCollection 2022 Mar 29.
To improve virtual screening for drug discovery, we present a collaborative approach between explainable artificial intelligence (AI) and simplified chemical interaction scores to efficiently search for active ligands bound to the target receptor. In particular, we focus on cyclin-dependent kinase 2 (CDK2), which is well known as a cancer target protein. Docking simulation alone is insufficient to distinguish active ligands from decoy molecules. To identify active ligands, in this paper, machine learning is employed together with scoring functions that simplify the screened Coulomb and Lennard-Jones interactions between the ligands and residues of the target receptor. We demonstrate that these simplified interaction scores can significantly improve the classification ability of machine learning models. We also demonstrate that explainable AI together with the simplified scoring method can highlight the important residues of CDK2 for recognizing active ligands.
为了改进用于药物发现的虚拟筛选，我们提出了一种可解释人工智能（AI）与简化化学相互作用评分之间的协作方法，以有效搜索与靶标受体结合的活性配体。特别是，我们专注于细胞周期蛋白依赖性激酶2（CDK2），它是众所周知的癌症靶标蛋白。仅靠对接模拟不足以区分活性配体和诱饵分子。为了识别活性配体，本文将机器学习与评分函数结合使用，该评分函数简化了配体与靶标受体残基之间筛选出的库仑相互作用和 Lennard-Jones 相互作用。我们证明这些简化的相互作用评分可以显著提高机器学习模型的分类能力。我们还证明，可解释人工智能与简化评分方法相结合，可以突出 CDK2 识别活性配体的重要残基。
