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

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 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

SUBMITTER: Song Y 

PROVIDER: S-EPMC10529232 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature