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SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemi...

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Autores principales: Patel, Hitesh, Ihlenfeldt, Wolf-Dietrich, Judson, Philip N., Moroz, Yurii S., Pevzner, Yuri, Peach, Megan L., Delannée, Victorien, Tarasova, Nadya I., Nicklaus, Marc C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658252/
https://www.ncbi.nlm.nih.gov/pubmed/33177514
http://dx.doi.org/10.1038/s41597-020-00727-4
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author Patel, Hitesh
Ihlenfeldt, Wolf-Dietrich
Judson, Philip N.
Moroz, Yurii S.
Pevzner, Yuri
Peach, Megan L.
Delannée, Victorien
Tarasova, Nadya I.
Nicklaus, Marc C.
author_facet Patel, Hitesh
Ihlenfeldt, Wolf-Dietrich
Judson, Philip N.
Moroz, Yurii S.
Pevzner, Yuri
Peach, Megan L.
Delannée, Victorien
Tarasova, Nadya I.
Nicklaus, Marc C.
author_sort Patel, Hitesh
collection PubMed
description We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.
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spelling pubmed-76582522020-11-17 SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules Patel, Hitesh Ihlenfeldt, Wolf-Dietrich Judson, Philip N. Moroz, Yurii S. Pevzner, Yuri Peach, Megan L. Delannée, Victorien Tarasova, Nadya I. Nicklaus, Marc C. Sci Data Data Descriptor We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738. Nature Publishing Group UK 2020-11-11 /pmc/articles/PMC7658252/ /pubmed/33177514 http://dx.doi.org/10.1038/s41597-020-00727-4 Text en © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, 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 metadata files associated with this article.
spellingShingle Data Descriptor
Patel, Hitesh
Ihlenfeldt, Wolf-Dietrich
Judson, Philip N.
Moroz, Yurii S.
Pevzner, Yuri
Peach, Megan L.
Delannée, Victorien
Tarasova, Nadya I.
Nicklaus, Marc C.
SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules
title SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules
title_full SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules
title_fullStr SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules
title_full_unstemmed SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules
title_short SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules
title_sort savi, in silico generation of billions of easily synthesizable compounds through expert-system type rules
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658252/
https://www.ncbi.nlm.nih.gov/pubmed/33177514
http://dx.doi.org/10.1038/s41597-020-00727-4
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