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: The present disclosure provides systems and methods for predicting olfactory properties of a molecule. One example method includes obtaining a machine-learned graph neural network trained to predict olfactory properties of molecules based at least in part on chemical structure data associated with the molecules. The method includes obtaining a graph that graphically describes a chemical structure of a selected molecule. The method includes providing the graph as input to the machine-learned graph neural network. The method includes receiving prediction data descriptive of one or more predicted olfactory properties of the selected molecule as an output of the machine-learned graph neural network. The method includes providing the prediction data descriptive of the one or more predicted olfactory properties of the selected molecule as an output.