Bitter taste involves the detection of diverse chemical compounds by a family of G protein‐coupled receptors, known as taste receptor type 2 (TAS2R). It is often linked to toxins and harmful compounds and in particular bitter taste receptors participate in the regulation of glucose homeostasis, modulation of immune and inflammatory responses, and may have implications for various diseases. Human TAS2Rs are characterized by their polymorphism and differ in localization and function. Different receptors can activate various signaling pathways depending on the tissue and the ligand. However, in vitro screening of possible TAS2R ligands is costly and time‐consuming. For this reason, in silico methods to predict bitterant‐TAS2R interactions could be powerful tools to help in the selection of ligands and targets for experimental studies and improve our knowledge of bitter receptor roles. Machine learning (ML) is a branch of artificial intelligence that applies algorithms to large datasets to learn from patterns and make predictions. In recent years, there has been a record of numerous taste classifiers in literature, especially on bitter/non‐bitter or bitter/sweet classification. However, only a few of them exploit ML to predict which TAS2R receptors could be targeted by bitter molecules. Indeed, the shortage and incompleteness of data on receptor‐ligand associations in literature make this task non‐trivial. In this work, we provide an overview of the state of the art dealing with this specific investigation, focusing on three ML‐based models, namely BitterX (2016), BitterSweet (2019) and BitterMatch (2022). This review aims to establish the foundation for future research endeavours focused on addressing the limitations and drawbacks of existing models.

中文翻译：

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机器学习方法预测苦味剂的 TAS2R 受体

苦味涉及 G 蛋白偶联受体家族（称为 2 型味觉受体 (TAS2R)）对多种化学物质的检测。它通常与毒素和有害化合物有关，特别是苦味受体参与葡萄糖稳态的调节、免疫和炎症反应的调节，并可能对各种疾病产生影响。人类 TAS2R 的特点是多态性，并且定位和功能不同。不同的受体可以根据组织和配体激活不同的信号通路。然而，体外筛选可能的 TAS2R 配体既昂贵又耗时。因此，预测苦味剂与 TAS2R 相互作用的计算机方法可能是有力的工具，有助于选择实验研究的配体和靶点，并提高我们对苦味受体作用的了解。机器学习 (ML) 是人工智能的一个分支，它将算法应用于大型数据集以从模式中学习并做出预测。近年来，文献中记录了许多味道分类器，特别是苦/非苦或苦/甜分类器。然而，只有少数人利用机器学习来预测苦味分子可能针对哪些 TAS2R 受体。事实上，文献中受体-配体关联数据的缺乏和不完整使得这项任务变得非同小可。在这项工作中，我们概述了处理这项具体调查的最新技术，重点关注三个基于 ML 的模型，即 BitterX (2016)、BitterSweet (2019) 和 BitterMatch (2022)。本综述旨在为未来的研究工作奠定基础，重点关注解决现有模型的局限性和缺点。