While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning. Machine learning is centered around the development and applications of algorithms that can learn from data automatically. Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies. This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.

中文翻译：

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自动发现GPCR生物活性配体。

尽管G蛋白偶联受体（GPCR）构成了最大的膜蛋白类别，但大部分GPCR的结构和内源性配体仍然未知。由于GPCR参与了各种信号传导途径和生理作用，因此内源性配体的鉴定以及设计新药对研究和医学界都非常感兴趣。除了强调基于结构的配体发现的最新进展（包括对接和分子动力学）外，本文还将重点介绍使用机器学习自动发现生物活性配体的最新进展。机器学习围绕可以自动从数据中学习的算法的开发和应用为中心。这种方法为生物活性预测以及定量的构效关系研究提供了巨大的机会。这篇综述描述了机器学习在生物活性配体发现中的最新成功应用，并总结了深度学习方法的前景，这些方法能够从结构数据中自动提取重要信息，作为快速有效的生物活性配体发现的有希望的未来方向。