network with LSTM cells. The neural network was composed of four layers with a total of 5,820,515 parameters (layer 1: BatchNormalization, layer 2: 1024 LSTM cells, layer 3: 256 LSTM cells, and layer 4: BatchNormalization) and trained with SMILES data encoded as one-hot vectors. The CLM was trained using the Adam optimizer (learning rate = 10 -3 ) and the categorical cross-entropy loss function. Training was performed over 40 epochs, where one epoch was defined as one pass over all the training data. Transfer learning was performed by keeping the parameters of the first network layer constant and training the second layer with a learning rate of 10 -4 .