Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China.
School of Bioinformatics, Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Food Res Int. 2022 Mar;153:110974. doi: 10.1016/j.foodres.2022.110974. Epub 2022 Feb 5.
Identifying the taste characteristics of molecules is essential for the expansion of their application in health foods and drugs. It is time-consuming and consumable to identify the taste characteristics of a large number of compounds through experiments. To date, computational methods have become an important technique for identifying molecular taste. In this work, bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener are predicted using three structure-taste relationship models based on the convolutional neural networks (CNN), multi-layer perceptron (MLP)-Descriptor, and MLP-Fingerprint. The results showed that all three models have unique characteristics in the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener. For the prediction of bitterant/non-bitterant, sweetener/non-sweetener, and bitterant/sweetener, the MLP-Fingerprint model exhibited a higher predictive AUC value (0.94, 0.94 and 0.95) than the MLP-Descriptor model (0.94, 0.84 and 0.87) and the CNN model (0.88, 0.90 and 0.91) by external validation, respectively. The MLP-Descriptor model showed a distinct structure-taste relationship of the studied molecules, which helps to understand the key properties associated with bitterants and sweeteners. The CNN model requires only a simple 2D chemical map as input to automate feature extraction for favorable prediction. The obtained models achieved accurate predictions of bitterant/non-bitterant, sweetener/non-sweetener and bitterant and sweetener, providing vital references for the identification of bioactive molecules and toxic substances.
确定分子的味觉特征对于扩大其在保健品和药物中的应用至关重要。通过实验来确定大量化合物的味觉特征既耗时又耗物。迄今为止，计算方法已成为鉴定分子味觉的重要技术。在这项工作中，使用基于卷积神经网络（CNN）、多层感知机（MLP）-Descriptor 和 MLP-Fingerprint 的三种结构-味觉关系模型来预测苦/非苦、甜/非甜和苦/甜。结果表明，这三个模型在预测苦/非苦、甜/非甜和苦/甜方面都具有独特的特征。对于苦/非苦、甜/非甜和苦/甜的预测，MLP-Fingerprint 模型在外部验证时表现出更高的预测 AUC 值（0.94、0.94 和 0.95），优于 MLP-Descriptor 模型（0.94、0.84 和 0.87）和 CNN 模型（0.88、0.90 和 0.91）。MLP-Descriptor 模型显示出研究分子的明显结构-味觉关系，有助于了解与苦味剂和甜味剂相关的关键性质。CNN 模型仅需要简单的 2D 化学图作为输入，即可自动进行特征提取，有利于进行有利的预测。所获得的模型实现了对苦/非苦、甜/非甜和苦/甜的准确预测，为生物活性分子和有毒物质的鉴定提供了重要参考。
