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CReM: chemically reasonable mutations framework for structure generation

Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On th...

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Autor principal: Polishchuk, Pavel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178718/
https://www.ncbi.nlm.nih.gov/pubmed/33430959
http://dx.doi.org/10.1186/s13321-020-00431-w
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author Polishchuk, Pavel
author_facet Polishchuk, Pavel
author_sort Polishchuk, Pavel
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description Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.
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spelling pubmed-71787182020-04-26 CReM: chemically reasonable mutations framework for structure generation Polishchuk, Pavel J Cheminform Software Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space. Springer International Publishing 2020-04-22 /pmc/articles/PMC7178718/ /pubmed/33430959 http://dx.doi.org/10.1186/s13321-020-00431-w Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Software
Polishchuk, Pavel
CReM: chemically reasonable mutations framework for structure generation
title CReM: chemically reasonable mutations framework for structure generation
title_full CReM: chemically reasonable mutations framework for structure generation
title_fullStr CReM: chemically reasonable mutations framework for structure generation
title_full_unstemmed CReM: chemically reasonable mutations framework for structure generation
title_short CReM: chemically reasonable mutations framework for structure generation
title_sort crem: chemically reasonable mutations framework for structure generation
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178718/
https://www.ncbi.nlm.nih.gov/pubmed/33430959
http://dx.doi.org/10.1186/s13321-020-00431-w
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