Protein-ligand binding affinity prediction model based on graph attention network

Abstract

Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to improve the performance of classical scoring functions, has attracted many scientists' attention. In this paper, we have developed an affinity prediction model called GAT-Score based on graph attention network (GAT). The protein-ligand complex is represented by a graph structure, and the atoms of protein and ligand are treated in the same manner. Two improvements are made to the original graph attention network. Firstly, a dynamic feature mechanism is designed to enable the model to deal with bond features. Secondly, a virtual super node is introduced to aggregate node-level features into graph-level features, so that the model can be used in the graph-level regression problems. PDBbind database v.2018 is used to train the model. Finally, the performance of GAT-Score was tested by the scheme $C_s$ (Core set as the test set) and CV (Cross-Validation). It has been found that our results are better than most methods from machine learning models with traditional molecular descriptors.

Keywords

• binding affinity
• structure-based drug design
• graph attention network
• scoring function
• machine learning