The training data was passed to the RNN multiple times: each iteration, called an epoch, all compounds in the set were input to the RNN. To enhance the learning process, the learning rate (LR) was reduced after every epoch. This hyperparameter controls the optimization speed of the learning process, higher LRs imply faster learning but less refined solutions. After some early testing it was observed that a LR greater than 10 -3 and smaller than 10 -5 have no effect on the training whatsoever, so the LR changes from 10 -3 to 10 -5 , being multiplied by a constant every epoch.