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.

中文翻译：

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机器学习在GPCR生物活性配体发现中的应用。

GPCR构成了最大的可药物治疗家族，目标是475种获得美国食品和药物管理局（FDA）批准的药物。由于GPCR对制药业非常感兴趣，因此人们花费了巨大的努力来寻找相关且有效的GPCR配体作为先导化合物。不同化学数据库中存在数千万种化合物。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药物发现流程的重要组成部分。ML方法在基于配体和基于结构的虚拟筛选中都有应用。我们在这里提供化学信息学概述ML在GPCR药物发现的不同阶段的应用。专注于嗅觉受体，这是GPCR的最大家族，