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Focused Library Generator: case of Mdmx inhibitors

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Abstract

We present a Focused Library Generator that is able to create from scratch new molecules with desired properties. After training the Generator on the ChEMBL database, transfer learning was used to switch the generator to producing new Mdmx inhibitors that are a promising class of anticancer drugs. Lilly medicinal chemistry filters, molecular docking, and a QSAR IC50 model were used to refine the output of the Generator. Pharmacophore screening and molecular dynamics (MD) simulations were then used to further select putative ligands. Finally, we identified five promising hits with equivalent or even better predicted binding free energies and IC50 values than known Mdmx inhibitors. The source code of the project is available on https://github.com/bigchem/online-chem.

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Acknowledgements

This study was partially supported by the China Scholarship Council (CSC) for providing the fellowship for Miss. Xia (201706880010) and ERA-CVD (httpd://era-cvd.eu) "Cardio-Oncology" project, BMBF 01KL1710. The authors also express gratitude to NVIDIA Corporation for donating Titan V, Xp and Quadro P6000 graphics cards for this research.

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  1. Institute of Structural Biology, Helmholtz Zentrum München - Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

    Zhonghua Xia, Pavel Karpov, Grzegorz Popowicz & Igor V. Tetko

  2. BigChem GmbH, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

    Pavel Karpov & Igor V. Tetko

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  1. Zhonghua Xia
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  2. Pavel Karpov
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Xia, Z., Karpov, P., Popowicz, G. et al. Focused Library Generator: case of Mdmx inhibitors. J Comput Aided Mol Des 34, 769–782 (2020). https://doi.org/10.1007/s10822-019-00242-8

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