Assessing and improving the performance of consensus docking strategies using the DockBox package
Molecular docking is a well-established computational technique that aims to predict how a ligand binds to a specific protein target, as well as to assess the strength of the binding. Although docking programs are used worldwide for drug discovery, it is not always simple to identify which program or combination of programs provides the best results for a target of interest. Here we present DockBox, a computational package designed to facilitate the use of multiple docking and scoring programs allowing to combine them using different consensus strategies. As part of the DockBox package, a new consensus docking method called score-based consensus docking (SBCD) is introduced. SBCD was found to significantly improve the pose prediction success rates of single docking programs. When applied to virtual screening, SBCD enhanced enrichment factors while producing higher hit rates than standard consensus docking (CD). SBCD can be run with almost no additional computational cost and time compared to CD, if the same docking programs are used for pose generation. Furthermore, SBCD allows the use of many scoring functions to assess consensus without significant overhead, making it a promising new approach for the screening of large chemical libraries. DockBox is an open-source package publicly available at https://pypi.org/project/dockbox.
KeywordsDocking software Scoring functions Consensus docking Computer-aided drug discovery Virtual screening Binding pose prediction
We gratefully acknowledge support from the PSMN (Pôle Scientifique de Modélisation Numérique) of the ENS de Lyon for the computing resources. We also warmly thank Prof. Jack Tuszynski and Mr. Philip Winter for providing useful comments and proofreading the manuscript.
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
- 1.Preto J, Gentile F, Winter P et al (2018) Molecular dynamics and related computational methods with applications to drug discovery. In: Bonilla LL, Kaxiras E, Melnik R (eds) Coupled mathematical models for physical and biological nanoscale systems and their applications. Springer, Cham, pp 267–285CrossRefGoogle Scholar
- 2.Morris GM, Lim-Wilby M (2008) Molecular Docking. Methods in molecular biology (Clifton, N.J.). Humana Press, Totowa, pp 365–382Google Scholar
- 25.Case D., Berryman JT, Betz RM, et al (2015) Amber 15. In: Univ. California, San Fr. http://ambermd.org/. Accessed 31 Oct 2015
- 36.Chemical Computing Group Inc. Montreal, QC, Canada (2015) Molecular Operating Environment 2015 (MOE 2015)Google Scholar
- 39.Schrödinger LLC (2019) Small-molecule drug discovery Suite 2019-1Google Scholar
- 40.Morris GM, Goodsell DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14%3c1639:AID-JCC10%3e3.0.CO;2-B CrossRefGoogle Scholar
- 42.Pedregosa F, Varoquaux G, Gramfort A et al (2012) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830Google Scholar