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Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

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Abstract

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

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Acknowledgements

The authors would like to thank Ivan Gushchin from MIPT Moscow for providing his expertise in crystallography. This work was partially supported by the Ministry of Education and Science of the Russian Federation (Grant No. 6.3157.2017).

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Authors and Affiliations

  1. Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France

    Maria Kadukova & Sergei Grudinin

  2. Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700

    Maria Kadukova & Vladimir Chupin

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  1. Maria Kadukova
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  2. Vladimir Chupin
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  3. Sergei Grudinin
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Correspondence to Sergei Grudinin.

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Kadukova, M., Chupin, V. & Grudinin, S. Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. J Comput Aided Mol Des 34, 191–200 (2020). https://doi.org/10.1007/s10822-019-00263-3

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  • DOI: https://doi.org/10.1007/s10822-019-00263-3

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