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

  • Jin Li
  • Ailing Fu
  • Le ZhangEmail author
Review
  • 16 Downloads

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.

Keywords

Molecular docking Scoring function Ligand pose Binding affinity Protein–ligand interaction 

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

Notes

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).

Author contributions

Conception and design: LZ; Writing and revision of the manuscript: JL; ALF.

Funding

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).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.

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Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.School of Medical Information and EngineeringSouthwest Medical UniversityLuzhouChina
  3. 3.College of Pharmaceutical SciencesSouthwest UniversityChongqingChina
  4. 4.College of Computer ScienceSichuan UniversityChengduChina
  5. 5.Medical Big Data CenterSichuan UniversityChengduChina
  6. 6.Zdmedical, Information Polytron Technologies Inc ChongqingChongqingChina

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