CLM implementation and training. All computational experiments were implemented in Python (v3.6.5) using Keras (v2.2.0, ) with the Tensorflow GPU backend (v1.9.0, ). CLMs were implemented using a recurrent neural network with long short-term memory cells (LSTM) . The network, which was composed of four layers comprising 5,820,515 parameters (layer 1: batch normalization; layer 2: LSTM with 1024 units; layer 3: LSTM with 256 units; layer 4: batch normalization), was trained with SMILES strings encoded as onehot vectors. We used the Adam optimizer with a learning rate of 10 -4 for the CLM training during 90 epochs , where one epoch was defined as one pass over all the training data. Fine-tuning was performed by further training the CLM on the fine-tuning set for 100 epochs.