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Title: OnionNet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells
Authors: Wang, Zechen
Zheng, Liangzhen
Liu, Yang
Qu, Yuanyuan
Li, Yong-Qiang
Zhao, Mingwen
Mu, Yuguang
Li, Weifeng
Keywords: Science::Biological sciences
Issue Date: 2021
Source: Wang, Z., Zheng, L., Liu, Y., Qu, Y., Li, Y., Zhao, M., Mu, Y. & Li, W. (2021). OnionNet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells. Frontiers in Chemistry, 9, 753002-.
Project: RG146/17 
Journal: Frontiers in Chemistry 
Abstract: One key task in virtual screening is to accurately predict the binding affinity (△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 △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.
ISSN: 2296-2646
DOI: 10.3389/fchem.2021.753002
Schools: School of Biological Sciences 
Rights: © 2021 Wang, Zheng, Liu, Qu, Li, Zhao, Mu and Li . This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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