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Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

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Huang, Sheng-You, Grinter, Sam Z., Zou, Xiaoqin·2010·Physical Chemistry Chemical Physics

DOI 10.1039/c0cp00151aPMC PMC11103779

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Abstract

The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking. The commonly-used assessment criteria and publicly available protein-ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

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Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research
Doss, C. G. P., Chakraborty, C., Narayan, V., & Kumar, D. T. (2014) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-800168-4.00010-x · Cited by 27
Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins
Sciortino, G., Garribba, E., Rodrı́guez-Guerra, J., & Maréchal, J. (2019) · ACS Omega · doi:10.1021/acsomega.8b03457 · Cited by 27
Improving the binding affinity estimations of protein–ligand complexes using machine-learning facilitated force field method
Soni, A., Bhat, R., & Jayaram, B. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00305-1 · Cited by 26
“However, in these complexes, none of the molecules (protein/ligand) is in randomized assembly of atoms as in case of liquids; in fact, these molecules are constrained by the covalent bonds and thus, the considered reference state will not be defined as a true reference state [5, 28, 29].”
Background
Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015
Xu, X., Yan, C., & Zou, X. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0038-1 · Cited by 28
In vitro anti-Leishmania activity and molecular docking of spiro-acridine compounds as potential multitarget agents against Leishmania infantum
Almeida, F. S., Sousa, G. L. S., Rocha, J. C., Ribeiro, F. F., de Oliveira, M. R., Grisi, T. C. S. D. L., Araújo, D. A. M., Nobre, M. S. D. C., Castro, R. N., do Amaral, I. P. G., et al. (2021) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2021.128289 · Cited by 26
Discovery of Novel Disruptor of Silencing Telomeric 1-Like (DOT1L) Inhibitors using a Target-Specific Scoring Function for the (S)-Adenosyl- l -methionine (SAM)-Dependent Methyltransferase Family
Wang, Y., Li, L., Zhang, B., Xing, J., Chen, S., Wan, W., Song, Y., Jiang, H., Jiang, H., Luo, C., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01785 · Cited by 26
A workflow to create a high-quality protein–ligand binding dataset for training, validation, and prediction tasks
Wang, Y., Sun, K., Li, J., Guan, X., Zhang, O., Bagni, D., Zhang, Y., Carlson, H. A., & Head-Gordon, T. L. (2025) · Digital Discovery · doi:10.1039/d4dd00357h · Cited by 28
Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?
Korb, O., ten Brink, T., Devadoss, F. R., Keil, M., & Exner, T. E. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9539-5 · Cited by 26
In Silico ADME Techniques Used in Early-Phase Drug Discovery
Danielson, M. L., Hu, B., Shen, J., & Desai, P. V. (2017) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-3-319-50042-3_4 · Cited by 25
Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches
Lam, J. H., & Katritch, V. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00031-4 · Cited by 30
Toward a Benchmarking Data Set Able to Evaluate Ligand- and Structure-based Virtual Screening Using Public HTS Data
Lindh, M., Svensson, F., Schaal, W., Zhang, J., Sköld, C., Brandt, P., & Karlén, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci5005465 · Cited by 25
In silico identification and evaluation of potential interaction of Azadirachta indica phytochemicals with Plasmodium falciparum heat shock protein 90
Daniyan, M. O., & Ojo, O. T. (2018) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2018.11.017 · Cited by 25
Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels
Pliushcheuskaya, P., & Künze, G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24119226 · Cited by 24
“Physics-based scoring functions rely on force field parameters that are retrieved from experimental data and quantum mechanical calculations [70].”
Method
Virtual Screening and Molecular Dynamics Study of Potential Negative Allosteric Modulators of mGluR1 from Chinese Herbs
Jiang, L., Zhang, X.-B., Chen, X., He, Y., Qiao, L., Zhang, Y., Li, G., & Xiang, Y. (2015) · Molecules · doi:10.3390/molecules200712769 · Cited by 24
“00 Å), was the optimal combination [24].”
HotLig: A Molecular Surface-Directed Approach to Scoring Protein–Ligand Interactions
Wang, S., Wu, Y., Kuo, S., & Yu, J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400302d · Cited by 24
In Silico and in Vitro Physicochemical Screening of Rigidoporus sp. Crude Laccase-assisted Decolorization of Synthetic Dyes—Approaches for a Cost-effective Enzyme-based Remediation Methodology
Sridhar, S., Chinnathambi, V., Arumugam, P., & Suresh, P. K. (2013) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-012-0041-x · Cited by 24
Enhancing Generalizability in Protein–Ligand Binding Affinity Prediction with Multimodal Contrastive Learning
Luo, D., Liu, D., Qu, X., Dong, L., & Wang, B. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01961 · Cited by 21
Rational Prediction with Molecular Dynamics for Hit Identification
Nichols, S. E., Swift, R. V., & Amaro, R. E. (2012) · Current Topics in Medicinal Chemistry · doi:10.2174/156802612804910313 · Cited by 22
“Today, these force field descriptions are generally known as “scoring functions” [30, 31] and may be augmented by a variety of energetic terms, such as solvation and entropy; these typically provide a rough estimate of binding affinity.”
Background
Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence
Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 28
Automated Large-Scale File Preparation, Docking, and Scoring: Evaluation of ITScore and STScore Using the 2012 Community Structure–Activity Resource Benchmark
Grinter, S. Z., Yan, C., Huang, S., Jiang, L., & Zou, X. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400045v · Cited by 22
Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement
Zhang, J., Li, H., Zhao, X., Wu, Q., & Huang, S. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00895 · Cited by 21
In silico protein function prediction: the rise of machine learning-based approaches
Chen, J., Gu, Z., Lai, L., & Pei, J. (2023) · Medical Review · doi:10.1515/mr-2023-0038 · Cited by 23
“The disadvantage of molecular docking-based methods is that they require accurate protein structure as input and are time-consuming [102 – 104].”
Method
Construction and Test of Ligand Decoy Sets Using MDock: Community Structure–Activity Resource Benchmarks for Binding Mode Prediction
Huang, S., & Zou, X. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200080g · Cited by 22
Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks
Yan, C., Grinter, S. Z., Merideth, B. R., Ma, Z., & Zou, X. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00504 · Cited by 21
A pose prediction approach based on ligand 3D shape similarity
Kumar, A., & Zhang, K. Y. J. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9923-2 · Cited by 22
Virtual Screening for Potential Allosteric Inhibitors of Cyclin-Dependent Kinase 2 from Traditional Chinese Medicine
Lü, F., Luo, G., Qiao, L., Jiang, L., Li, G., & Zhang, Y. (2016) · Molecules · doi:10.3390/molecules21091259 · Cited by 22
Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity PredictionPreprint
Li, J., Guan, X., Zhang, O., Sun, K., Wang, Y., Bagni, D., & Head-Gordon, T. L. (2023) · PubMed · doi:10.48550/arxiv.2308.09639 · Cited by 22
Computational Approaches to the Chemical Equilibrium Constant in Protein‐ligand Binding
Montalvo‐Acosta, J. J., & Cecchini, M. (2016) · Molecular Informatics · doi:10.1002/minf.201600052 · Cited by 24
Determination of Ligand Binding Epitope Structures Using Polarization Transfer from Hyperpolarized Ligands
Wang, Y., & Hilty, C. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01711 · Cited by 21

Authors: Huang, S.-Y., Grinter, S. Z., & Zou, X.
Affiliations: University of Missouri (US)
Corresponding author: Zou, Xiaoqin
ORCID: Huang, Sheng-You
Grinter, Sam Z.
Zou, Xiaoqin
Venue: Physical Chemistry Chemical Physics
Publisher: Royal Society of Chemistry (RSC)
Published: 2010
Volume: 12
Issue: 40
Pages: 12899
ISSN: 1463-9076 (ISSN-L)
1463-9084
DOI: 10.1039/c0cp00151a
PMC: PMC11103779
OpenAlex: W2038542598
Cited by: 508
References: 133
Citations by year: 2026: 27 · 2025: 34 · 2024: 33 · 2023: 34 · 2022: 45 · 2021: 44 · 2020: 31 · 2019: 34 · 2018: 30 · 2017: 22 · 2016: 37 · 2015: 34 · 2014: 42 · 2013: 27 · 2012: 20
FWCI: 1.66
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Physical Chemistry Chemical Physics (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of General Medical Sciences (R21GM088517)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
Keywords: protein-ligand docking, scoring functions, knowledge-based scoring, consensus scoring, structure-based drug design, protein-ligand binding affinity prediction, benchmarking database
MeSH terms: Algorithms, Computer Simulation, Ligands, Protein Binding, Proteins, Drug Design

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Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

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