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

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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).

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).

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Jin Li & Le Zhang

  2. School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China

    Jin Li

  3. College of Pharmaceutical Sciences, Southwest University, Chongqing, 400715, China

    Ailing Fu

  4. College of Computer Science, Sichuan University, Chengdu, 610065, China

    Le Zhang

  5. Medical Big Data Center, Sichuan University, Chengdu, 610065, China

    Le Zhang

  6. Zdmedical, Information Polytron Technologies Inc Chongqing, Chongqing, 401320, China

    Le Zhang

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  1. Jin Li
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  2. Ailing Fu
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  3. Le Zhang
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Conception and design: LZ; Writing and revision of the manuscript: JL; ALF.

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Correspondence to Le Zhang.

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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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