Protein-Ligand Binding Affinity Prediction Using Deep Learning

Abstract

Protein-ligand prediction plays a key role in drug discovery. Nevertheless, many algorithms are over reliant on 3D structure representations of proteins and ligands which are often rare. Techniques that can leverage the sequence-level representations of proteins, ligands and pockets are thus required to predict binding affinity and facilitate the drug discovery process. We have proposed a deep learning model with an attention mechanism to predict protein-ligand binding affinity. Our model is able to make comparable achievements with state-of-the-art deep learning models used for protein-ligand binding affinity prediction.