基于分类的机器学习方法来预测分子的味道:一篇评论
Cristian Rojas1, Davide Ballabio2, Viviana Consonni2
1Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca 010107, Ecuador.
Food research international (Ottawa, Ont.)
|June 18, 2023
概括
从分子结构来预测味觉对安全和进化至关重要. 本综述涵盖了40年的定量结构-味道关系建模,从早期的基于连接体的分类器到现代机器学习方法.
科学领域:
- * 感官科学和计算化学.
- * 了解味觉的生物和化学基础.
背景情况:
- * 味觉受体对于物种的生存至关重要,它可以区分安全和危险的化合物.
- * 味觉指导行为,影响食物的选择和避免毒素.
- *口味被分为基本 (甜,苦,乌玛米,酸,) 和非基本类型.
研究的目的:
- * 审查定量结构-味道关系 (QSTR) 建模的历史发展.
- * 探索机器学习应用在预测化学结构的味道特性方面的演变.
- * 提供QSTR建模的概述,从它的开始到最近的进展.
主要方法:
- *对QSTR建模研究的文献评论.
- * 基于分类的机器学习方法的分析.
- *追踪从早期的基于带 (LB) 的模型到当前的方法的进展.
主要成果:
- *自1980年首个LB分类器以来,QSTR建模领域已经发生了显著的发展.
- * 机器学习技术在预测口味类方面变得越来越复杂.
- *最近的研究 (至2022年) 证明了基于分子结构的先进预测能力.
结论:
- * QSTR建模是预测味觉的强大工具.
- * 计算方法可以加速发现和设计具有所需味道特征的化合物.
- *在QSTR建模方面的持续研究提高了我们对化学感应和分子相互作用的理解.
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