Furthermore, we investigated attention-based modeling in which the neural network architecture is extended to search for parts of the input sequences that are relevant to the target variable. In case of LSTM networks, the attention mechanism gives importance to certain parts of the sequence (i.e., SMILES) rather than considering the whole sequence as important. For this purpose, we implemented Multiplicative Attention from Keras Self-Attention library with regularization and without any attention bias.