The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening
Open accessAbstract
Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.
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Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
A semiempirical free energy force field with charge‐based desolvation
Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20634 · Cited by 2,246
Benchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318
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“We used LeDock version 1.0 (Zhao and Huang, 2011), rDock version 2013.1 (Ruiz-Carmona et al., 2014), AutoDock Vina version 1.1.2 (Trott and Olson, 2010), and smina version ‘‘Oct 15 2019’’ (Koes et al., 2013), the last of which implemented the DK and Vinardo (Quiroga and Villarreal, 2016) scoring functions in addition…”
BackgroundMethodInfluentialUni-Mol: A Universal 3D Molecular Representation Learning Framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81
AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
Trepte, P., Secker, C., Olivet, J., Blavier, J., Kostova, S., Maseko, S. B., Minia, I., Silva Ramos, E., Cassonnet, P., Golusik, S., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00019-8 · Cited by 35
Minisini, M., Di Giorgio, E., Kerschbamer, E., Dalla, E., Faggiani, M., Franforte, E., Meyer-Almes, F.-J., Ragno, R., Antonini, L., Mai, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac081 · Cited by 28
Metadata
- Authors
- Quiroga, R., & Villarreal, M. A.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-05-12
- Volume
- 11
- Issue
- 5
- Pages
- e0155183
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2388080941
- Cited by
- 398
- References
- 4
- FWCI
- 7.44
- Type
- journal article
- Language
- en
- Open access
- gold
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