Predicting the sensory properties of compounds is challenging, due to the subjective nature of the experimental measurements, which rely on a panel of human participants and are therefore also expensive and time-consuming. 

In this webinar we describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network.