Abstract：
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.
摘要：
苦味涉及G蛋白偶联受体家族对多种化合物的检测，称为味觉受体2型（TAS2R）。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TAS2R的特征在于它们的多态性并且在定位和功能上不同。不同的受体可以根据组织和配体激活各种信号通路。然而,可能的TAS2R配体的体外筛选是昂贵且耗时的。出于这个原因,预测苦味-TAS2R相互作用的计算机模拟方法可能是强大的工具，有助于选择配体和靶标进行实验研究，并提高我们对苦味受体作用的认识。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进行预测。近年来,文献中有许多口味分类器的记录，特别是在苦/非苦或苦/甜分类上。然而,他们中只有少数人利用ML来预测哪些TAS2R受体可以被苦味分子靶向。的确,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不平凡.在这项工作中,我们概述了处理这一具体调查的最新技术，专注于三种基于机器学习的模型，即BitterX(2016)，BitterSweet（2019）和BitterMatch（2022）。这篇综述旨在为未来的研究工作奠定基础，重点是解决现有模型的局限性和缺点。
