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Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.
Open accessAbstract
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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“In this context, scoring functions emerge as a straightforward and fast strategy despite limited accuracy, remaining as the main alternative to be applied in VS experiments (Huang et al., 2010).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Huang et al., 2010
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Virtual Screening in Drug Design
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Khoury, L. E., Santos‐Martins, D., Sasmal, S., Eberhardt, J., Bianco, G., Ambrosio, F. A., Solis-Vasquez, L., Koch, A., Forli, S., & Mobley, D. L. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00240-w · Cited by 78
Wang, D. D., Zhu, M., & Yan, H. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa107 · Cited by 76
Score_set: A CAPRI benchmark for scoring protein complexes
Lensink, M. F., & Wodak, S. J. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24678 · Cited by 71
“9 The energy terms may represent physics-based residue or atom pair potentials,10,11 or a statistical simplification thereof.”
BackgroundComprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges
Huang, S. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx030 · Cited by 79
Practical Considerations in Virtual Screening and Molecular Docking
Berry, M. J., Fielding, B. C., & Gamieldien, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-802508-6.00027-2 · Cited by 77
“The GB model is also more efficient and faster at ranking binding affinities of ligands, making it more suitable in a virtual screening context (Hou et al. , 2011a, 2011b; Huang et al. , 2010; Li et al. , 2010).”
MethodYang, C., & Zhang, Y. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00485 · Cited by 74
Integrated Molecular Modeling and Machine Learning for Drug Design
Xia, S., Chen, E., & Zhang, Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00814 · Cited by 75
Xu, W., Lucke, A. J., & Fairlie, D. P. (2015) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2015.01.009 · Cited by 74
Converging a Knowledge-Based Scoring Function: DrugScore2018
Dittrich, J., Schmidt, D., Pfleger, C., & Gohlke, H. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00582 · Cited by 69
Molecular Docking: A Structure-Based Approach for Drug Repurposing
Kumar, S., & Kumar, S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00006-7 · Cited by 81
Automated docking for novel drug discovery
Bello, M., Martínez‐Archundia, M., & Correa‐Basurto, J. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.794780 · Cited by 71
“Despite its simple physical meaning, a major challenge for force field scoring functions is how to treat the solvent in ligand binding [114] .”
BackgroundVirtual Screening Strategies in Medicinal Chemistry: The State of the Art and Current Challenges
Braga, R. C., Alves, V. M., Silva, A. C., do Nascimento, M. N., Silva, F. M., Lião, L. M., & Andrade, C. H. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026614666140929120749 · Cited by 74
Sarkar, M., Paul, S. S., & Mukherjea, K. K. (2013) · Journal of Luminescence · doi:10.1016/j.jlumin.2013.03.026 · Cited by 70
Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides
Zhou, P., Li, B., Yan, Y., Jin, B., Wang, L., & Huang, S. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00142 · Cited by 68
Efficient conformational ensemble generation of protein-bound peptides
Yan, Y., Zhang, D., & Huang, S. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0246-7 · Cited by 70
“Because of its high computational efficiency, ensemble docking has been widely used to consider molecular flexibility in both protein– protein and protein–ligand docking [10, 26, 27].”
The latest automated docking technologies for novel drug discovery
Caballero, J. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1858793 · Cited by 69
“Despite the great amount of innovative ideas reflected in too many different scoring functions, it is common to classify them into empirical, force field-based, and knowledge-based functions [15].”
BackgroundVirtual Screening with Gnina 1.0
Sunseri, J., & Koes, D. (2021) · Molecules · doi:10.3390/molecules26237369 · Cited by 71
“A “scoring function” maps the input structure representing the relative location and orientation of the pair of molecules to a score representing the strength of their interaction[1].”
BackgroundIn silico methods for design of biological therapeutics
Roy, A., Nair, S. A., Sen, N., Soni, N., & Madhusudhan, M. S. (2017) · Methods · doi:10.1016/j.ymeth.2017.09.008 · Cited by 62
Paul, B. K., Ray, D., & Guchhait, N. (2012) · Physical Chemistry Chemical Physics · doi:10.1039/c2cp23496c · Cited by 62
Moon, S., Hwang, S.-Y., Lim, J., & Kim, W. Y. (2023) · Digital Discovery · doi:10.1039/d3dd00149k · Cited by 67
Metadata
- Authors
- Huang, S.-Y., Grinter, S. Z., & Zou, X.
- Affiliations
- University of Missouri (US)
- Corresponding author
- 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
- 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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