Software for molecular docking: a review
Open accessAbstract
Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.
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Antibody-antigen docking addresses the lack of specific interaction information from paratope and epitope prediction.
“This issue is addressed by antibody–antigen docking, a specialized application of the broader field of molecular docking [170] (Figure 2B).”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [170]
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Drug repurposing: progress, challenges and recommendations
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“This issue is addressed by antibody–antigen docking, a specialized application of the broader field of molecular docking [170] (Figure 2B).”
BackgroundUnleashing the power of generative AI in drug discovery
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Drug databases and their contributions to drug repurposing
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Biology and medicine in the landscape of quantum advantages
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- Authors
- Pagadala, N. S., Syed, K., & Tuszynski, J.
- Affiliations
- University of Alberta (CA)
- Central University of Technology (ZA)
- Corresponding author
- Pagadala, Nataraj S.
- Venue
- Biophysical Reviews
- Published
- 2017-01-16
- Volume
- 9
- Issue
- 2
- Pages
- 91-102
- ISSN
- 1867-2450 (ISSN-L)
- 1867-2469
- PMC
- PMC5425816
- OpenAlex
- W2578119541
- Cited by
- 1,522
- References
- 130
- Citations by year
- 2026: 181 · 2025: 187 · 2024: 193 · 2023: 187 · 2022: 231 · 2021: 199 · 2020: 154 · 2019: 114 · 2018: 64 · 2017: 12
- FWCI
- 9.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Biophysical Reviews (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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 binding, drug discovery, homology modeling, druggability, lead optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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