  Motivation: Prediction of the interaction affinity between proteins and
compounds is a major challenge in the drug discovery process. WideDTA is a
deep-learning based prediction model that employs chemical and biological
textual sequence information to predict binding affinity.
  Results: WideDTA uses four text-based information sources, namely the protein
sequence, ligand SMILES, protein domains and motifs, and maximum common
substructure words to predict binding affinity. WideDTA outperformed one of the
state of the art deep learning methods for drug-target binding affinity
prediction, DeepDTA on the KIBA dataset with a statistical significance. This
indicates that the word-based sequence representation adapted by WideDTA is a
promising alternative to the character-based sequence representation approach
in deep learning models for binding affinity prediction, such as the one used
in DeepDTA. In addition, the results showed that, given the protein sequence
and ligand SMILES, the inclusion of protein domain and motif information as
well as ligand maximum common substructure words do not provide additional
useful information for the deep learning model. Interestingly, however, using
only domain and motif information to represent proteins achieved similar
performance to using the full protein sequence, suggesting that important
binding relevant information is contained within the protein motifs and
domains.
