机器学习在 G 蛋白偶联受体生物活性配体发现中的应用。
Applications of machine learning in GPCR bioactive ligand discovery.
机构信息
Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
出版信息
Curr Opin Struct Biol. 2019 Apr;55:66-76. doi: 10.1016/j.sbi.2019.03.022. Epub 2019 Apr 18.
GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.
G 蛋白偶联受体（GPCRs）构成了最大的可成药家族，拥有 475 种美国食品和药物管理局（FDA）批准药物的靶点。由于 GPCRs 是制药行业非常感兴趣的目标，因此正在投入大量努力寻找相关的、有效的 GPCR 配体作为先导化合物。不同的化学数据库中存在着数千万种化合物。为了扫描这个巨大的化学空间，计算方法，特别是机器学习（ML）方法，是 GPCR 药物发现管道的重要组成部分。ML 方法在基于配体和基于结构的虚拟筛选中都有应用。我们在这里对 ML 在 GPCR 药物发现的不同阶段的应用进行了化学信息学概述。本研究以嗅觉受体（ORs）为例，ORs 是最大的 GPCR 家族，对一个异位嗅觉受体 OR1G1 的激动剂进行预测的案例研究，比较了四种经典的 ML 方法。
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