使用深度学习对乌玛米品尝者进行计算选

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Prantar Dutta¹, Kishore Gajula¹, Nitu Verma¹

¹Physical Sciences Research Area, TCS Research, Tata Consultancy Services, Pune, India.

Molecular diversity

|October 18, 2024

概括

研究人员开发了一个虚拟选管道,以发现新的乌玛米味道. 这种数据驱动的方法准确地识别了的味道分子,帮助食品行业寻找新的成分.

科学领域:

计算化学和化学信息学
感官科学和味觉感知是感官科学和味觉感知.
食品科学与技术食品科学与技术

背景情况:

乌玛米是一种关键的味道模式,对味的味道至关重要,但对乌玛米分子的知识仍然有限.
食品行业需要有效的方法来识别新的乌玛米味道,以提高产品的风味.
目前用于味道发现的方法通常是低效的,需要先进的计算方法.

研究的目的:

开发和验证一个虚拟选管道,以理性地发现有力的乌玛米品味剂.
创建机器学习模型来分类乌玛米分子并预测它们的效力.
建立一个端到端的计算框架,用于识别新的味味道化合物.

主要方法:

策划了439个乌玛米和428个非乌玛米分子的广泛数据集.
训练了一个基于变压器的结构,用于umami分子分类,达到93%的准确性.
开发了一个神经网络模型来预测乌玛米化合物的效力,结合相似性分析,毒性选和分子对接验证.

主要成果:

该分类模型准确地区分了乌玛米和非乌玛米分子.
开发的虚拟框架成功地选了FooDB数据库中的强有力的乌玛米化合物.
分子对接证实了选分子与乌玛米味觉受体的结合.

结论:

数据驱动的计算方法显示出发现基于分子特征的新品品味的巨大潜力.
拟议的虚拟选管道为食品行业的美味发现需求提供了有效的实施.
这项研究通过综合计算方法推进了风味化合物的理性设计.

关键词:

计算选进行了计算选.深度学习是一种深度学习.这就是QSPR.一个调味器.乌玛米 (Umami) 是一个美丽的城市.

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