Sweetness is the most crucial sensory attribute of compounds that add calories and nutritional value to the food. Sweet amalgams are highly employed throughout the food industry and have a significant impact on human health. Over-consumption of these sweeteners can lead to lifestyle disorders such as type-2 diabetes, heart disease, and other obesity-related diseases. Hence, building computational model to predict the sweetness value of the compounds towards discovering compounds that are healthier is of foremost importance. The model works by assimilation of features chemical generated from Mordred and Padel.

Gradient Boost and Random Forest Regressor outperform other models with correlation coefficient and root mean square error of 0.94, 0.23 and 0.92, 0.28, respectively.