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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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Chung, M. L., Park, S., & Lee, H. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01259-6
“…methods approximate affinity using predefined interaction terms and are commonly formulated as force-field– based, empirical, or knowledge-based scoring functions [9, 10, 12], yet their fixed functional forms constrain their capacity to capture complex or atypical interaction patterns [10].”
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InfluentialNative Contact Ratio as a Topological Metric for Machine Learning Based Molecular Docking
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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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