Chemical language model. We used our recently published framework to implement the CLM () , which is based on a long shortterm memory (LSTM) . The model implementations differed based on the transfer learning strategy used: (i) For single fine-tuning, the model was composed of four layers that have a total of 5,820,515 parameters (layer 1: BatchNormalization, layer 2: LSTM with 1,024 units, layer 3: LSTM with 256 units and layer 4: BatchNormalization). We pretrained the CLM for 10 epochs with a learning rate of 10 -3 and performed the finetuning for 16 epochs with a learning rate of 10 -4 by keeping layer 2 frozen. (ii) For double fine-tuning, the model was composed of layers that have a total of 8,444,003 parameters (layer 1: BatchNormalization, layer 2: LSTM with 1,024 units, layer 3: LSTM with 512 units and layer 4: LSTM with 256 units, layer 5: BatchNormalization). We pretrained the CLM for 10 epochs with a learning rate of 10 -3 and performed two rounds of fine-tuning. The first round was applied for 20 epochs with a learning rate of 10 -4 by keeping layer 2 frozen. The second round was applied for 20 epochs with a learning rate of 10 -4 by keeping layer 2 and 3 frozen. The CLMs for both strategies were trained on SMILES strings encoded as one-hot vectors. We used the categorical cross-entropy loss and the Adam optimizer . We applied a 10-fold data augmentation to all molecules.