VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds

Abstract

Taste is one of the crucial organoleptic properties involved in the perception of food by humans. Taste of a chemical compound present in food stimulates us to take in food and avoid poisons. Bitter taste of drugs presents compliance problems and early flagging of potential bitterness of a drug candidate may help with its further development. Similarly, the taste of chemicals present in food is important for evaluation of food quality in the industry. In this work, we have implemented machine learning models to predict three different taste endpoints-sweet, bitter and sour. The VirtualTaste models achieved an overall accuracy of 90% and an AUC of 0.98 in 10-fold cross-validation and in an independent test set. The web server takes a two-dimensional chemical structure as input and reports the chemical's taste profile for three tastes-using molecular fingerprints along with confidence scores, including information on similar compounds with known activity from the training set and an overall radar chart. Additionally, insights into 25 bitter receptors are also provided via target prediction for the predicted bitter compounds. VirtualTaste, to the best of our knowledge, is the first freely available web-based platform for the prediction of three different tastes of compounds. It is accessible via http://virtualtaste.charite.de/VirtualTaste/without any login requirements and is free to use.