Open Access
RSC Advances, volume 11, issue 56, pages 35383-35391
Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds
1
Meton AI, Inc., Wilmington, DE, USA
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3
Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas Y Técnicas (CONICET), Ciudad de Buenos Aires, Argentina
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Publication type: Journal Article
Publication date: 2021-11-02
Journal:
RSC Advances
Quartile SCImago
Q2
Quartile WOS
Q2
Impact factor: 3.9
ISSN: 20462069, 20462069
General Chemistry
General Chemical Engineering
Abstract
The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.
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Citations by publishers
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1 publication, 7.69%
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1 publication, 7.69%
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Scardino V. et al. Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds // RSC Advances. 2021. Vol. 11. No. 56. pp. 35383-35391.
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Scardino V., Bollini M., Cavasotto C. N. Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds // RSC Advances. 2021. Vol. 11. No. 56. pp. 35383-35391.
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TY - JOUR
DO - 10.1039/d1ra05785e
UR - https://doi.org/10.1039/d1ra05785e
TI - Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds
T2 - RSC Advances
AU - Scardino, Valeria
AU - Cavasotto, Claudio N.
AU - Bollini, Mariela
PY - 2021
DA - 2021/11/02 00:00:00
PB - Royal Society of Chemistry (RSC)
SP - 35383-35391
IS - 56
VL - 11
SN - 2046-2069
SN - 2046-2069
ER -
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@article{2021_Scardino,
author = {Valeria Scardino and Claudio N. Cavasotto and Mariela Bollini},
title = {Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds},
journal = {RSC Advances},
year = {2021},
volume = {11},
publisher = {Royal Society of Chemistry (RSC)},
month = {nov},
url = {https://doi.org/10.1039/d1ra05785e},
number = {56},
pages = {35383--35391},
doi = {10.1039/d1ra05785e}
}
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Scardino, Valeria, et al. “Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds.” RSC Advances, vol. 11, no. 56, Nov. 2021, pp. 35383-35391. https://doi.org/10.1039/d1ra05785e.