Abstract: A computer-implemented method for predicting whether a molecule will be a good mosquito repellent is disclosed. The method includes obtaining a machine-learned prediction model obtained by transfer learning. The model has been trained using a first, larger training dataset for an odour prediction task and with a second, smaller training dataset for predicting whether a molecule would function as a mosquito repellent. The method further includes obtaining input data that describes a chemical structure of a selected molecule, providing the input data that describes the chemical structure of the selected molecule as input to the machine-learned prediction model, receiving prediction data descriptive of whether the selected molecule would be a good mosquito repellent as an output of the machine-learned sensory prediction model and providing the prediction data as output.

Abstract: Chemical formulation property prediction can involve understanding each molecule individually and the mixture as a whole. Machine-learned models can be utilized to extract individual and holistic data to generate accurate predictions of the properties of mixtures. Properties that can include, but are not limited to, olfactory properties, taste properties, color properties, viscosity properties, and other commercially, industrially, or pharmaceutically beneficial properties.