人工智能加速G蛋白偶联受体配体的发现

Wei Chen, Chi Song, Liang Leng, Sanyin Zhang, Shilin Chen

工程（英文） ›› 2024, Vol. 32 ›› Issue (1) : 18-28.

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工程（英文） ›› 2024, Vol. 32 ›› Issue (1) : 18-28. DOI: 10.1016/j.eng.2023.09.011
研究论文
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人工智能加速G蛋白偶联受体配体的发现

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The Application of Artificial Intelligence Accelerates G Protein-Coupled Receptor Ligand Discovery

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摘要

G蛋白偶联受体（GPCR）在多种生理过程中发挥着关键作用，是新药发现的重要靶标。然而，传统的GPCR配体发现方法需要投入大量的时间和资源。人工智能方法的出现为GPCR配体的识别和优化提供了有利的工具，改变了GPCR配体发现的研究方式。本文从数据资源、数据描述、模型设计等方面介绍了如何利用人工智能方法构建GPCR配体发现模型，并分析了人工智能方法在GPCR药物领域的应用；提出了一种基于人工智能方法整合多组学数据的GPCR配体筛选策略；探讨了人工智能方法在GPCR研究领域面临的挑战和未来发展方向。人工智能方法与多学科的交叉融合将提高GPCR配体发现的效率。

Abstract

G protein-coupled receptors (GPCRs) are crucial players in various physiological processes, making them attractive candidates for drug discovery. However, traditional approaches to GPCR ligand discovery are time-consuming and resource-intensive. The emergence of artificial intelligence (AI) methods has revolutionized the field of GPCR ligand discovery and has provided valuable tools for accelerating the identification and optimization of GPCR ligands. In this study, we provide guidelines for effectively utilizing AI methods for GPCR ligand discovery, including data collation and representation, model selection, and specific applications. First, the online resources that are instrumental in GPCR ligand discovery were summarized, including databases and repositories that contain valuable GPCR-related information and ligand data. Next, GPCR and ligand representation schemes that can convert data into computer-readable formats were introduced. Subsequently, the key applications of AI methods in the different stages of GPCR drug discovery were discussed, ranging from GPCR function prediction to ligand design and agonist identification. Furthermore, an AI-driven multi-omics integration strategy for GPCR ligand discovery that combines information from various omics disciplines was proposed. Finally, the challenges and future directions of the application of AI in GPCR research were deliberated. In conclusion, continued advancements in AI techniques coupled with interdisciplinary collaborations will offer great potential for improving the efficiency of GPCR ligand discovery.

关键词

G蛋白偶联受体 / 配体 / 人工智能 / 多组学 / 药物发现

Keywords

G protein-coupled receptor / Ligand / Artificial intelligence / Multi-omics / Drug discovery

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Wei Chen, Chi Song, Liang Leng. 人工智能加速GPCR配体的发现. Engineering. 2024, 32(1): 18-28 https://doi.org/10.1016/j.eng.2023.09.011

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