DockStream: a docking wrapper to enhance de novo molecular design

Abstract 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 maj...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jeff Guo, Jon Paul Janet, Matthias R. Bauer, Eva Nittinger, Kathryn A. Giblin, Kostas Papadopoulos, Alexey Voronov, Atanas Patronov, Ola Engkvist, Christian Margreitter
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	De novo design Generative Models Reinforcement Learning (RL) Molecular docking Structure-based drug discovery (SBDD) Information technology T58.5-58.64 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/52346401511c411199adbbd42216b9e7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:52346401511c411199adbbd42216b9e7
record_format	dspace
spelling	oai:doaj.org-article:52346401511c411199adbbd42216b9e72021-11-21T12:33:30ZDockStream: a docking wrapper to enhance de novo molecular design10.1186/s13321-021-00563-71758-2946https://doaj.org/article/52346401511c411199adbbd42216b9e72021-11-01T00:00:00Zhttps://doi.org/10.1186/s13321-021-00563-7https://doaj.org/toc/1758-2946Abstract 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 .Jeff GuoJon Paul JanetMatthias R. BauerEva NittingerKathryn A. GiblinKostas PapadopoulosAlexey VoronovAtanas PatronovOla EngkvistChristian MargreitterBMCarticleDe novo designGenerative ModelsReinforcement Learning (RL)Molecular dockingStructure-based drug discovery (SBDD)Information technologyT58.5-58.64ChemistryQD1-999ENJournal of Cheminformatics, Vol 13, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	De novo design Generative Models Reinforcement Learning (RL) Molecular docking Structure-based drug discovery (SBDD) Information technology T58.5-58.64 Chemistry QD1-999
spellingShingle	De novo design Generative Models Reinforcement Learning (RL) Molecular docking Structure-based drug discovery (SBDD) Information technology T58.5-58.64 Chemistry QD1-999 Jeff Guo Jon Paul Janet Matthias R. Bauer Eva Nittinger Kathryn A. Giblin Kostas Papadopoulos Alexey Voronov Atanas Patronov Ola Engkvist Christian Margreitter DockStream: a docking wrapper to enhance de novo molecular design
description	Abstract 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 .
format	article
author	Jeff Guo Jon Paul Janet Matthias R. Bauer Eva Nittinger Kathryn A. Giblin Kostas Papadopoulos Alexey Voronov Atanas Patronov Ola Engkvist Christian Margreitter
author_facet	Jeff Guo Jon Paul Janet Matthias R. Bauer Eva Nittinger Kathryn A. Giblin Kostas Papadopoulos Alexey Voronov Atanas Patronov Ola Engkvist Christian Margreitter
author_sort	Jeff Guo
title	DockStream: a docking wrapper to enhance de novo molecular design
title_short	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_sort	dockstream: a docking wrapper to enhance de novo molecular design
publisher	BMC
publishDate	2021
url	https://doaj.org/article/52346401511c411199adbbd42216b9e7
work_keys_str_mv	AT jeffguo dockstreamadockingwrappertoenhancedenovomoleculardesign AT jonpauljanet dockstreamadockingwrappertoenhancedenovomoleculardesign AT matthiasrbauer dockstreamadockingwrappertoenhancedenovomoleculardesign AT evanittinger dockstreamadockingwrappertoenhancedenovomoleculardesign AT kathrynagiblin dockstreamadockingwrappertoenhancedenovomoleculardesign AT kostaspapadopoulos dockstreamadockingwrappertoenhancedenovomoleculardesign AT alexeyvoronov dockstreamadockingwrappertoenhancedenovomoleculardesign AT atanaspatronov dockstreamadockingwrappertoenhancedenovomoleculardesign AT olaengkvist dockstreamadockingwrappertoenhancedenovomoleculardesign AT christianmargreitter dockstreamadockingwrappertoenhancedenovomoleculardesign
_version_	1718418941147086848

DockStream: a docking wrapper to enhance de novo molecular design

Ejemplares similares