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DockStream: a docking wrapper to enhance de novo molecular design

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstac...

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Autores principales: Guo, Jeff, Janet, Jon Paul, Bauer, Matthias R., Nittinger, Eva, Giblin, Kathryn A., Papadopoulos, Kostas, Voronov, Alexey, Patronov, Atanas, Engkvist, Ola, Margreitter, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596819/
https://www.ncbi.nlm.nih.gov/pubmed/34789335
http://dx.doi.org/10.1186/s13321-021-00563-7
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author Guo, Jeff
Janet, Jon Paul
Bauer, Matthias R.
Nittinger, Eva
Giblin, Kathryn A.
Papadopoulos, Kostas
Voronov, Alexey
Patronov, Atanas
Engkvist, Ola
Margreitter, Christian
author_facet Guo, Jeff
Janet, Jon Paul
Bauer, Matthias R.
Nittinger, Eva
Giblin, Kathryn A.
Papadopoulos, Kostas
Voronov, Alexey
Patronov, Atanas
Engkvist, Ola
Margreitter, Christian
author_sort Guo, Jeff
collection PubMed
description Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00563-7.
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spelling pubmed-85968192021-11-17 DockStream: a docking wrapper to enhance de novo molecular design Guo, Jeff Janet, Jon Paul Bauer, Matthias R. Nittinger, Eva Giblin, Kathryn A. Papadopoulos, Kostas Voronov, Alexey Patronov, Atanas Engkvist, Ola Margreitter, Christian J Cheminform Research Article Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00563-7. Springer International Publishing 2021-11-17 /pmc/articles/PMC8596819/ /pubmed/34789335 http://dx.doi.org/10.1186/s13321-021-00563-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Guo, Jeff
Janet, Jon Paul
Bauer, Matthias R.
Nittinger, Eva
Giblin, Kathryn A.
Papadopoulos, Kostas
Voronov, Alexey
Patronov, Atanas
Engkvist, Ola
Margreitter, Christian
DockStream: a docking wrapper to enhance de novo molecular design
title DockStream: a docking wrapper to enhance de novo molecular design
title_full DockStream: a docking wrapper to enhance de novo molecular design
title_fullStr DockStream: a docking wrapper to enhance de novo molecular design
title_full_unstemmed DockStream: a docking wrapper to enhance de novo molecular design
title_short DockStream: a docking wrapper to enhance de novo molecular design
title_sort dockstream: a docking wrapper to enhance de novo molecular design
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596819/
https://www.ncbi.nlm.nih.gov/pubmed/34789335
http://dx.doi.org/10.1186/s13321-021-00563-7
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