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Article

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy
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Int. J. Mol. Sci. 2019, 20(9), 2060; https://doi.org/10.3390/ijms20092060
Received: 26 February 2019 / Revised: 8 April 2019 / Accepted: 22 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design)
The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average. View Full-Text
Keywords: enrichment factor; virtual screening; molecular docking; rescore; consensus strategy enrichment factor; virtual screening; molecular docking; rescore; consensus strategy
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MDPI and ACS Style

Pedretti, A.; Mazzolari, A.; Gervasoni, S.; Vistoli, G. Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns. Int. J. Mol. Sci. 2019, 20, 2060. https://doi.org/10.3390/ijms20092060

AMA Style

Pedretti A, Mazzolari A, Gervasoni S, Vistoli G. Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns. International Journal of Molecular Sciences. 2019; 20(9):2060. https://doi.org/10.3390/ijms20092060

Chicago/Turabian Style

Pedretti, Alessandro, Angelica Mazzolari, Silvia Gervasoni, and Giulio Vistoli. 2019. "Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns" International Journal of Molecular Sciences 20, no. 9: 2060. https://doi.org/10.3390/ijms20092060

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