The DOI matches its Crossref record.
Performance Evaluation of Docking Programs- Glide, GOLD, AutoDock & SurflexDock, Using Free Energy Perturbation Reference Data: A Case Study of Fructose-1, 6-bisphosphatase-AMP Analogs
Abstract
Background: The accurate ranking of analogs of lead molecules with respect to their estimated binding free energies to drug targets remains highly challenging in molecular docking due to small relative differences in their free energy values. Methods: Free energy perturbation (FEP) method, which provides the most accurate relative binding free energy values were earlier used to calculate free energies of many ligands for several important drug targets including Fructose-1,6-BisphosPhatase (FBPase). The availability of abundant structural and experimental binding affinity data for FBPase inhibitors provided an ideal system to evaluate four widely used docking programs, AutoDock, Glide, GOLD and SurflexDock, distinct from earlier comparative evaluation studies. Results: The analyses suggested that, considering various parameters such as docking pose, scoring and ranking accuracy, sensitivity analysis and newly introduced relative ranking score, Glide provided reasonably consistent results in all respects for the system studied in the present work. Whereas GOLD and AutoDock also demonstrated better performance, AutoDock results were found to be significantly superior in terms of scoring accuracy compared to the rest. Conclusion: Present analysis serves as a useful guide for researchers working in the field of lead optimization and for developers in upgradation of the docking programs.
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“Software programs like AutoDock, GOLD, and Glide use docking simulations to predict the binding of algal peptides to target protein receptors. They provide a three-dimensional model of peptide-ligand interactions, scoring the binding affinity and identifying the most thermodynamically favorable conformation [212,213].”
From Navigating the potential of algal peptides: health effects, market applications, and scientific challenges (2026) · cites this paper as [212]
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“These related software programs include Gold, AutoDock, GLIDE, etc. [78].”
MethodZhang, Y., Li, H., Hai, X., Guo, X., & Di, X. (2024) · Journal of Chromatography A · doi:10.1016/j.chroma.2024.465084 · Cited by 37
Singh, S. C., Singh, S. C., Khatri, D. K., et al. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07741 · Cited by 17
Luo, F.-H., Zhao, Z., Yong, H., et al. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.169349 · Cited by 14
Chua, H. M., Moshawih, S., Kifli, N., Goh, H. P., & Ming, L. C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301396 · Cited by 11
“More recently, Reddy et al. demonstrated that Glide gave consistent results in terms of docking conformation, ranking and scoring accuracy, but AutoDock ranked as the best scoring accuracy among all other tested software [53].”
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Application of Docking for Lead Optimization
Patra, J., Singh, D., Jain, S., & Mahindroo, N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00012-6 · Cited by 8
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BackgroundKhaliq, M., Noor, Z., Moazzam, M., Saeed, F., Tul-Zohra, K., Tariq, E., Muhammad Shahbaz, H., & Khaliq, K. (2026) · Annals of Medicine · doi:10.1080/07853890.2026.2637282 · Cited by 4
“They provide a three-dimensional model of peptide-ligand interactions, scoring the binding affinity and identifying the most thermodynamically favorable conformation [212,213].”
Navarrete‐Mondragón, R. D. C., Cortés‐Benítez, F., Mendieta‐Wejebe, J. E., et al. (2024) · ChemMedChem · doi:10.1002/cmdc.202400452 · Cited by 3
Sundriyal, S., Eeda, V., Lagisetty, P., et al. (2020) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2020.115874 · Cited by 3
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Lytan, I., Singha, B., Boruah, N., Gogoi, P. P., Longkumer, P., Pucho, M., Dkhar, B., & Sinha, U. B. (2026) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/0113895575418750251204104229 · Cited by 1
Imran, A. Q. M. A., Sarker, S., Priya, S. N., et al. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109159 · Cited by 1
ElSawy, K. M., Alminderej, F. M., Verma, C. S., & Caves, L. S. D. (2022) · Molecular Systems Design & Engineering · doi:10.1039/d2me00042c
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From genomes to interventions: computational strategies transforming parasitology
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Metadata
- Authors
- Reddy, K. K., Rathore, R. S., Srujana, P., Burri, R. R., Reddy, C. R., Sumakanth, M., Reddanna, P., & Reddy, M. R.
- Affiliations
- University of Hyderabad (IN)
- International Institute of Information Technology, Hyderabad (IN)
- Rational Labs (p) Ltd., Plot #177, IDA Mallapur, Hyderabad-500 076, India
- Osmania University (IN)
- Corresponding author
- Reddanna, Pallu
- Venue
- Mini-Reviews in Medicinal Chemistry
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2020-07-23
- Volume
- 20
- Issue
- 12
- Pages
- 1179-1187
- ISSN
- 1389-5575 (ISSN-L)
- OpenAlex
- W3032176118
- Cited by
- 18
- References
- 0
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2022: 1 · 2021: 2 · 2020: 1
- FWCI
- 0.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Mini-Reviews in Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- 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
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- glides, gold, AutoDock, free energy perturbation, binding free energy, fructose-1,6-bisphosphatase, ligand ranking, lead optimization
- MeSH terms
- Adenosine Monophosphate, Binding Sites, Fructose-Bisphosphatase, Ligands, Protein Binding, Software, Thermodynamics, Drug Design, Molecular Docking Simulation
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