Abstract
Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein–ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.
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Abbreviations
- SF:
-
Scoring function
- QM:
-
Quantum mechanics
- MM:
-
Molecular mechanics
- SVM:
-
Support vector machine
- RF:
-
Random forest
- ANN:
-
Artificial neural network
- DL:
-
Deep learning
- DNN:
-
Deep neural networks
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Acknowledgements
This study is supported by the National Natural Science Foundation of China (No. 61372138), and National Science and Technology Major Project of China (No. 2018ZX10201002).
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This study is supported by the National Natural Science Foundation of China (No. 61372138), and National Science and Technology Major Project of China (No. 2018ZX10201002).
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Conception and design: LZ; Writing and revision of the manuscript: JL; ALF.
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Li, J., Fu, A. & Zhang, L. An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking. Interdiscip Sci Comput Life Sci 11, 320–328 (2019). https://doi.org/10.1007/s12539-019-00327-w
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DOI: https://doi.org/10.1007/s12539-019-00327-w