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ChemFlow_py: a flexible toolkit for docking and rescoring

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Abstract

The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py.

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Acknowledgements

This work was funded by the French National Research Agency (ANR) through the Programme d'Investissement d'Avenir under contract 17-EURE- 0016 and received financial support from the Fondation pour la Recherche Med́icale (Grant DBI20141231319). The project received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie Grant Agreement 956314 [ALLODD]. Computational resources and support at the high-performance computing center (Mesocentre) of the University of Strasbourg are gratefully acknowledged.

Funding

Funding was supported by Agence Nationale de la Recherche,17-EURE-0016, Fondation pour la Recherche Medicale,DBI20141231319, and European Union’s Horizon 2020, 956314 [ALLODD].

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Authors and Affiliations

  1. Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France

    Luca Monari, Katia Galentino & Marco Cecchini

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  1. Luca Monari
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  2. Katia Galentino
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  3. Marco Cecchini
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Contributions

L.M. and M.C. conceived the study, designed the simulation protocol, analysed the data and wrote the main manuscript text. L.M. and K.G. performed the experiments and wrote the code. All authors reviewed and commented on the manuscript.

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Correspondence to Marco Cecchini.

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Monari, L., Galentino, K. & Cecchini, M. ChemFlow_py: a flexible toolkit for docking and rescoring. J Comput Aided Mol Des 37, 565–572 (2023). https://doi.org/10.1007/s10822-023-00527-z

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