Predicting Multiple Taste Sensations with a Multiobjective Machine Learning Method

Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds' tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement by developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing three different tastes, i.e., bitter, sweet, and umami, from other taste sensations. The developed model has been integrated into a publicly accessible web platform. This work lays the groundwork for a comprehensive understanding of the molecular features that drive the perception of tastes, paving the way towards new methodologies in the rational design of foods, such as the pre-determination of specific tastes, the engineering of complementary diets to traditional pharmacological treatments, and many others.