In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.

中文翻译：

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用于生物活性配体发现和 GPCR 配体识别的机器学习和基于人工智能的方法

在过去的十年中，机器学习和人工智能应用在学术研究和工业界的表现和关注度都得到了显着提升。最近大多数最先进方法背后的成功可以归因于深度学习的最新发展。当应用于涉及非表格数据（例如图像或文本）处理的各种科学领域时，深度学习已被证明不仅优于传统的机器学习，而且优于领域专家开发的高度专业化的工具。本综述旨在总结基于人工智能的 GPCR 生物活性配体发现研究，特别关注最新成果和研究趋势。为了使广大计算科学家能够理解本文，我们提供了对基础方法的指导性解释，包括对最常用的深度学习架构和分子数据特征表示的概述。我们重点介绍基于人工智能的最新研究，这些研究成功发现了 GPCR 生物活性配体。然而，本次综述的另一个重点是讨论基于机器学习的技术，该技术已普遍应用于配体发现，并有可能为未来成功发现 GPCR 生物活性配体铺平道路。这篇综述最后简要展望了深度学习的最新研究趋势，例如主动学习和半监督学习，它们在推进生物活性配体发现方面具有巨大潜力。