An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking

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 gener...

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Bibliographic Details
Published in:Interdisciplinary sciences : computational life sciences 2019-06, Vol.11 (2), p.320-328
Main Authors: Li, Jin, Fu, Ailing, Zhang, Le
Format: Article
Language:English
Subjects:
Algorithms
Binding
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Appl. in Life Sciences
Drug discovery
Health Sciences
Knowledge
Learning algorithms
Life Sciences
Ligands
Machine Learning
Mathematical and Computational Physics
Medicine
Molecular docking
Molecular Docking Simulation
Molecular modelling
Neural Networks, Computer
Protein Binding
Proteins
Proteins - metabolism
Research Design
Review
Reviews
Statistics for Life Sciences
Theoretical
Theoretical and Computational Chemistry
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Summary: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.
ISSN:1913-2751
1867-1462
DOI:10.1007/s12539-019-00327-w