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摘要
从结构设计到临床试验,人工智能方法,特别是机器学习,一直在药物开发中发挥着举足轻重的作用。 由于候选药物的大量可用数据集及其新的复杂信息解释方式,这种方法正在利用计算机辅助药物发现的影响,以确定研究范围的模式。 在本综述中,评估了与药物发现和治疗相关的近期应用,并分析了局限性和未来前景。
关键词: 机器学习、人工智能、药物再利用、虚拟筛选、ADMET、合成规划、个性化医疗、药物设计。
Current Medicinal Chemistry
Title:New Perspectives on Machine Learning in Drug Discovery
Volume: 28 Issue: 32
关键词: 机器学习、人工智能、药物再利用、虚拟筛选、ADMET、合成规划、个性化医疗、药物设计。
摘要: Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to the clinical trial. This approach is harnessing the impact of computer-aided drug discovery due to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed.
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Cite this article as:
New Perspectives on Machine Learning in Drug Discovery, Current Medicinal Chemistry 2021; 28 (32) . https://dx.doi.org/10.2174/0929867327666201111144048
DOI https://dx.doi.org/10.2174/0929867327666201111144048 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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