  One key task in virtual screening is to accurately predict the binding
affinity ($\triangle$$G$) of protein-ligand complexes. Recently, deep learning
(DL) has significantly increased the predicting accuracy of scoring functions
due to the extraordinary ability of DL to extract useful features from raw
data. Nevertheless, more efforts still need to be paid in many aspects, for the
aim of increasing prediction accuracy and decreasing computational cost. In
this study, we proposed a simple scoring function (called OnionNet-2) based on
convolutional neural network to predict $\triangle$$G$. The protein-ligand
interactions are characterized by the number of contacts between protein
residues and ligand atoms in multiple distance shells. Compared to published
models, the efficacy of OnionNet-2 is demonstrated to be the best for two
widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model
was further verified by non-experimental decoy structures from docking program
and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which
showed great success. Thus, our study provides a simple but efficient scoring
function for predicting protein-ligand binding free energy.
