Development of Deep Learning approaches to predict relationships between chemical structures and sweetness

Abstract

The non-caloric sweeteners market is catching up with the market of conventionally used sugars due to the benefits of preventing obesity, tooth decay and other health problems. Developing strategies for designing easier-to-produce novel molecules with a sweet taste and less toxicity are up-to-date motivations for the food industry. In this sense, Machine Learning (ML) approaches have been reported as cutting-edge technologies to guide the design of new molecules towards specific objectives, including sweet taste. The largest known dataset of sweet molecules is here provided. The dataset contains fully integrated 9541 sweeteners and 1141 bitterants from FooDB, FlavorDB and literature. This robust dataset allowed the development of standard Machine and Deep Learning pipelines towards conceiving Structure-Activity Relationships (SAR) between molecules and sweetness. In this work, we showcase that Textual Convolutional Neural Networks (TextCNN), Graph Convolutional Networks (GCN), and Deep Neural Networks (DNNs) outperformed most of traditional “shallow” learning approaches. These Deep Learning (DL) models produced platforms to guide the design of new sweeteners and repurposing existing compounds. Sixty million compounds from PubChem were evaluated using these models. Herein, we deliver a dataset of 67724 compounds that present high probabilities of being sweet. Quick searches in literature allowed us to find 13 molecules reported as potent sweetening agents, revealing that our approach is suitable for finding new sweeteners, valuable to expand food chemistry databases, repurposing existing chemicals and designing novel molecules with a sweet taste.