期刊
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
卷 11, 期 2, 页码 320-328出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s12539-019-00327-w
关键词
Molecular docking; Scoring function; Ligand pose; Binding affinity; Protein-ligand interaction
资金
- National Natural Science Foundation of China [61372138]
- National Science and Technology Major Project of China [2018ZX10201002]
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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