TY - JOUR
T1 - DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures
AU - Bao, Jingxiao
AU - He, Xiao
AU - Zhang, John Z.H.
N1 - Funding Information:
This work was supported by the National Key R&D Program of China (grant nos. 2016YFA0501700 and 2019YFA0905201), the National Natural Science Foundation of China (grant nos. 21922301 21761132022, 21673074, 91753103, and 21933010), the Natural Science Foundation of Shanghai Municipality (grant no. 18ZR1412600), and the Fundamental Research Funds for the Central Universities. We also thank NYU Shanghai and the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001) for providing computer resources.
Funding Information:
This work was supported by the National Key R&D Program of China (grant nos. 2016YFA0501700 and 2019YFA0905201), the National Natural Science Foundation of China (grant nos. 21922301, 21761132022, 21673074, 91753103, and 21933010), the Natural Science Foundation of Shanghai Municipality (grant no. 18ZR1412600), and the Fundamental Research Funds for the Central Universities. We also thank NYU Shanghai and the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001) for providing computer resources.
Publisher Copyright:
© 2021 American Chemical Society.
PY - 2021/5/24
Y1 - 2021/5/24
N2 - In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy structural information in their training data. Here, we developed a machine-learning model, named DeepBSP, that can directly predict the root mean square deviation (rmsd) of a ligand docking pose with reference to its native binding pose. Unlike the binding affinity, the rmsd between the docking poses with reference to their native structures can be straightforwardly determined. By training on a generated data set with 11,925 native complexes and more than 165,000 docked poses, our model shows excellent docking power on our test set and also on the CASF-2016 docking decoy set compared to other major scoring functions. Thus, by combining molecular dockings that generate many poses with the application of DeepBSP, one can more accurately predict the best binding pose that is closest to the native complex structure. This DeepBSP model shall be very useful in picking out poses close to their natives from many poses generated from a dock application.
AB - In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy structural information in their training data. Here, we developed a machine-learning model, named DeepBSP, that can directly predict the root mean square deviation (rmsd) of a ligand docking pose with reference to its native binding pose. Unlike the binding affinity, the rmsd between the docking poses with reference to their native structures can be straightforwardly determined. By training on a generated data set with 11,925 native complexes and more than 165,000 docked poses, our model shows excellent docking power on our test set and also on the CASF-2016 docking decoy set compared to other major scoring functions. Thus, by combining molecular dockings that generate many poses with the application of DeepBSP, one can more accurately predict the best binding pose that is closest to the native complex structure. This DeepBSP model shall be very useful in picking out poses close to their natives from many poses generated from a dock application.
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U2 - 10.1021/acs.jcim.1c00334
DO - 10.1021/acs.jcim.1c00334
M3 - Article
C2 - 33979150
AN - SCOPUS:85106531585
SN - 1549-9596
VL - 61
SP - 2231
EP - 2240
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 5
ER -