Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.
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DOI：
10.1093/bib/bby061
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发表时间：
2019-09-27
影响因子：
9.5
通讯作者：
Doğan T
中科院分区：
文献类型：
--
作者：
Rifaioglu AS;Atas H;Martin MJ;Cetin-Atalay R;Atalay V;Doğan T
确定药物/化合物与其靶点之间的相互作用对新药的开发至关重要。体外筛选实验（即生物测定）经常用于此目的；然而，实验方法不足以探索新的药物-靶标相互作用，主要是因为可行性问题，因为它们是劳动密集型的，昂贵的和耗时的。在过去的几十年里出现了一个被称为“虚拟筛选”（VS）的计算领域，通过统计估计化合物和生物靶点之间未知的生物相互作用来帮助实验性药物发现研究。这些方法利用化合物和/或靶蛋白的物理化学和结构特性以及实验验证的生物相互作用信息来生成预测模型。近年来，复杂的机器学习技术被应用于VS中，以提高预测性能。本研究的目的是检查和讨论机器学习技术在VS中的最新应用，包括深度学习，深度学习在计算机视觉和自然语言处理领域产生划时代的发展后变得非常流行。在过去的三年中，有大量的研究考虑了深度学习在生物医学中的应用，包括计算药物发现。在这篇综述中，我们首先描述了VS方法的主要工具，包括化合物和蛋白质特征（即表示和描述符），VS常用的库和工具包，生物活性数据库和用于系统训练和基准测试的金标准数据集。我们随后回顾了最近的VS研究，重点是深度学习应用。最后，我们讨论了该领域的现状，包括当前的挑战和建议未来的方向。我们相信这一调查将为在计算药物发现领域工作的研究人员在理解和开发新的生物预测方法方面提供见解。
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as ‘virtual screening’ (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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影响因子：
5.6
作者：
Baroni, Massimo;Cruciani, Gabriele;Mason, Jonathan S.
通讯作者：
Mason, Jonathan S.
影响因子：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium
影响因子：
5.6
作者：
Bender, Andreas;Jenkins, Jeremy L.;Davies, John W.
通讯作者：
Davies, John W.
影响因子：
5.8
作者：
Bjorkholm, Patrik;Daniluk, Pawel;Hvidsten, Torgeir R.
通讯作者：
Hvidsten, Torgeir R.
影响因子：
8.6
作者：
Bajusz D;Rácz A;Héberger K
通讯作者：
Héberger K