Song, Y.; Chang, S.; Tian, J.; Pan, W.; Feng, L.; Ji, H. A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods2023, 12, 3386.
Song, Y.; Chang, S.; Tian, J.; Pan, W.; Feng, L.; Ji, H. A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods 2023, 12, 3386.
Song, Y.; Chang, S.; Tian, J.; Pan, W.; Feng, L.; Ji, H. A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods2023, 12, 3386.
Song, Y.; Chang, S.; Tian, J.; Pan, W.; Feng, L.; Ji, H. A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods 2023, 12, 3386.
Abstract
Taste determination in small molecules is critical in food chemistry, but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as val-uable 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 2,601 molecules. The results reveal that GNN-based models outperform other ap-proaches in taste prediction. Moreover, consensus models that combine diverse molecular repre-sentations demonstrate improved performance. Among these, molecular fingerprints + GNN con-sensus 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.
Keywords
Cheminformatics; Taste prediction; Machine learning; Deep learning; Molecular feature representation
Subject
Chemistry and Materials Science, Food Chemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
