WideDTA: prediction of drug-target binding affinity

Understand

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.