  The prediction of protein interactions (CPIs) is crucial for the in-silico
screening step in drug discovery. Recently, many end-to-end representation
learning methods using deep neural networks have achieved significantly better
performance than traditional machine learning algorithms. Much effort has
focused on the compound representation or the information extraction from the
compound-protein interaction to improve the model capability by taking the
advantage of the neural attention mechanism. However, previous studies have
paid little attention to representing the protein sequences, in which the
long-range interactions of residue pairs are essential for characterizing the
structural properties arising from the protein folding. We incorporate the
self-attention mechanism into the protein representation module for CPI
modeling, which aims at capturing the long-range interaction information within
proteins. The proposed module concerning protein representation, called Protein
Transformer, with an integration with an existing CPI model, has shown a
significant improvement in the prediction performance when compared with
several existing CPI models.
