A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction

文献类型： 外文期刊

第一作者： Song, Yu

作者： Song, Yu;Feng, Lu;Song, Yu;Chang, Sihao;Tian, Jing;Pan, Weihua;Ji, Hongchao;Song, Yu;Chang, Sihao;Tian, Jing;Pan, Weihua;Ji, Hongchao

作者机构：

关键词： molecular feature representation; cheminformatics; taste prediction; machine learning; deep learning

期刊名称：FOODS （ 影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2023 年 12 卷 18 期

页码：

收录情况： SCI

摘要： Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.

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