An schematic representation of the DL predictive models used can be seen in Figure and. The amino acid sequence analysis block is common for both models and it is a Convolutional Recurrent Neural Network based on the one used by Jurtz et al for the prediction of subcellular localization of proteins 42 (Figure ). This architecture allows to build complex representations from both targets and compounds for the prediction of binding, in contrast to the LR models, which are directly fed with the input features. The input of the model is the amino acid sequence one-hot encoded, which is passed through a 1D Convolutional Neural Network (CNN). This 1D CNN comprises filters of sizes 1, 3, 5, 9, 15 and 21, with the aim of detecting motifs of di↵erent length in the amino acid sequence. Convolutional layers are followed then by a max pooling layer, downsampling the input and thus, reducing the number of model parameters. The input is then introduced to a bi-LSTM neural network. Dropout algorithm is used in di↵erent parts of the model to prevent overfitting. The compound analysis block depends on the encoding of molecules. If molecules are represented by their SMILES, then the compound processing block is similar to the sequence processing block (Figure ). The SMILES string is one-hot encoded and the input is passed to a bank of convolutional filters, in this case of size 3,4 and 5 based on the sizes of the LINGO substrings analysed by Vidal et al. After that, a maximum pooling layer condenses information and transfer it to a LSTM, but in this case uni-drectional since the SMILES strings are causal, in the sense that they are read in only one direction. Dropout is also used here to prevent overfitting. If molecules are represented by ECFP6, FCFP6 and HASHTT fingerprints, namely a binary vector of length 3072, the input is passed through a feed-forward neural network followed by dropout (Figure ).