College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Faculty of Applied Science, Macao Polytechnic University, Macao 999078.
Drug Discov Today. 2023 Nov;28(11):103796. doi: 10.1016/j.drudis.2023.103796. Epub 2023 Oct 5.
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.
激酶在调节几乎所有细胞过程中起着至关重要的作用，因此成为针对各种疾病的治疗干预的重要靶点。准确的激酶谱预测对于解决激酶药物发现中的选择性/特异性挑战至关重要，这与先导化合物优化、药物再利用以及对潜在药物副作用的理解密切相关。在这篇综述中，我们概述了基于机器学习 (ML) 和深度学习 (DL) 的定量构效关系 (QSAR) 模型在激酶谱预测方面的最新进展。我们强调了这个快速发展领域的当前趋势，并讨论了在实验数据集构建和模型架构设计方面存在的挑战和未来方向。我们的目的是为这些方法的开发和利用提供实用的见解和指导。
