Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data.
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data.
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DOI：
10.1016/j.drudis.2020.11.037
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发表时间：
2021-04
影响因子：
7.4
通讯作者：
Cortes-Ciriano I
中科院分区：
文献类型：
--
作者：
Bender A;Cortes-Ciriano I
药物发现数据与其他来源的数据在数量和特点上是不同的。本文强调了来自不同领域的数据的差异。为了充分受益于算法，我们需要解决数据带来的挑战。在数据匮乏的领域，数据不应脱离假设、其表示和方法。人工智能(AI)最近对图像和语音识别等领域产生了深刻的影响，这一进展已经转化为实际应用。然而，在药物发现领域，这样的进展仍然很少，其中一个原因是所用数据的内在原因。在这篇综述中，我们讨论了来自不同领域的数据的方面和差异，即图像、语音、化学和生物领域，可用的数据量，以及它们与药物发现的相关性。未来需要在我们对生物系统的理解方面有所改进，以及随后产生足够数量的实际相关数据，以真正推动人工智能在药物发现中的领域，使新化学的发现具有新的作用模式，这在临床上显示出理想的有效性和安全性。
Drug discovery data and data from other sources are different in quantity and characteristics. This article underlines the difference of data from different domains. In order to fully benefit from algorithms we need to address challenges posed by the data. Data should not be detached from a hypothesis, its representation, and the method in a data-scarce area. ‘Artificial Intelligence’ (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce, and one of the reasons is intrinsic to the data used. In this review, we discuss aspects of, and differences in, data from different domains, namely the image, speech, chemical, and biological domains, the amounts of data available, and how relevant they are to drug discovery. Improvements in the future are needed with respect to our understanding of biological systems, and the subsequent generation of practically relevant data in sufficient quantities, to truly advance the field of AI in drug discovery, to enable the discovery of novel chemistry, with novel modes of action, which shows desirable efficacy and safety in the clinic.
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