Journal
FRONTIERS IN CHEMISTRY
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fchem.2021.753002
Keywords
protein-ligand binding; deep learning; onionnet; residue-atom distance; structure-based affinity prediction
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Funding
- Natural Science Foundation of Shandong Province [ZR2020JQ04]
- National Natural Science Foundation of China [11874238]
- Singapore MOE Tier 1 Grant [RG146/17]
- Ministry of Education, Singapore, under its Academic Research Fund Tier 2 [MOE-T2EP30120-0007]
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The study introduces a scoring function OnionNet-2 based on deep learning for predicting protein-ligand binding free energy. The OnionNet-2 model demonstrates excellent performance on multiple datasets, showing high efficacy and success.
One key task in virtual screening is to accurately predict the binding affinity (oG) 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 oG. 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.
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