Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Med Res Rev. 2021 May;41(3):1427-1473. doi: 10.1002/med.21764. Epub 2020 Dec 9.
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
神经紊乱的数量明显多于其他治疗领域的疾病。然而，开发中枢神经系统 (CNS) 疾病的药物仍然是药物发现中最具挑战性的领域，伴随着漫长的时间线和高淘汰率。随着先进实验技术带来的生物医学数据的快速增长，人工智能 (AI) 和机器学习 (ML) 已经成为在药物发现中提取有意义的见解和改进决策的不可或缺的工具。得益于 AI 和 ML 算法的进步，现在 AI/ML 驱动的解决方案具有前所未有的潜力，可以提高 CNS 药物发现的成功率并加速其进程。在这篇综述中，我们全面总结了 AI/ML 驱动的药物发现工作及其在 CNS 领域的实施。在介绍了 AI/ML 模型以及概念化和数据准备之后，我们概述了 AI/ML 技术在药物发现的几个关键步骤中的应用，包括靶标识别、化合物筛选、命中/先导化合物生成和优化、药物反应和协同预测、从头药物设计和药物再利用。我们回顾了 AI/ML 指导 CNS 药物发现的最新进展，重点介绍了血脑屏障通透性预测及其在神经疾病治疗发现中的应用。最后，我们讨论了当前方法的主要挑战和局限性，以及可能为解决这些困难提供解决方案的未来方向。
