Models are trained for 1000 epochs with a learning rate of 0.000367 and a scheduler that reduces the learning rate by a factor of 0.7 whenever the loss plateaus for more than 20 epochs. Data augmentation is achieved by randomly rotating and translating the inputs with a maximum translation of 2 Å from the center of mass of the ligand.