For training the model, the input data is split into a 90:10 train:test ratio, and all descriptors other than fingerprints, i.e.non-binary descriptors, are scaled to unit variance. The model hyperparameters -regularization, activation function and the number and size of hidden layers, are optimized via ChemML's genetic algorithm module, which optimizes the mean absolute error (MAE) of the validation set obtained from a five-fold crossvalidation step in the training process .