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cheML.io: an online database of ML-generated molecules

Several recent ML algorithms for de novo molecule generation have been utilized to create an open-access database of virtual molecules. The algorithms were trained on samples from ZINC, a free database of commercially available compounds. Generated molecules, stemming from 10 different ML frameworks...

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Autores principales: Zhumagambetov, Rustam, Kazbek, Daniyar, Shakipov, Mansur, Maksut, Daulet, Peshkov, Vsevolod A., Fazli, Siamac
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058596/
https://www.ncbi.nlm.nih.gov/pubmed/35516285
http://dx.doi.org/10.1039/d0ra07820d
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author Zhumagambetov, Rustam
Kazbek, Daniyar
Shakipov, Mansur
Maksut, Daulet
Peshkov, Vsevolod A.
Fazli, Siamac
author_facet Zhumagambetov, Rustam
Kazbek, Daniyar
Shakipov, Mansur
Maksut, Daulet
Peshkov, Vsevolod A.
Fazli, Siamac
author_sort Zhumagambetov, Rustam
collection PubMed
description Several recent ML algorithms for de novo molecule generation have been utilized to create an open-access database of virtual molecules. The algorithms were trained on samples from ZINC, a free database of commercially available compounds. Generated molecules, stemming from 10 different ML frameworks, along with their calculated properties were merged into a database and coupled to a web interface, which allows users to browse the data in a user friendly and convenient manner. ML-generated molecules with desired structures and properties can be retrieved with the help of a drawing widget. For the case of a specific search leading to insufficient results, users are able to create new molecules on demand. These newly created molecules will be added to the existing database and as a result, the content as well as the diversity of the database keeps growing in line with the user's requirements.
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spelling pubmed-90585962022-05-04 cheML.io: an online database of ML-generated molecules Zhumagambetov, Rustam Kazbek, Daniyar Shakipov, Mansur Maksut, Daulet Peshkov, Vsevolod A. Fazli, Siamac RSC Adv Chemistry Several recent ML algorithms for de novo molecule generation have been utilized to create an open-access database of virtual molecules. The algorithms were trained on samples from ZINC, a free database of commercially available compounds. Generated molecules, stemming from 10 different ML frameworks, along with their calculated properties were merged into a database and coupled to a web interface, which allows users to browse the data in a user friendly and convenient manner. ML-generated molecules with desired structures and properties can be retrieved with the help of a drawing widget. For the case of a specific search leading to insufficient results, users are able to create new molecules on demand. These newly created molecules will be added to the existing database and as a result, the content as well as the diversity of the database keeps growing in line with the user's requirements. The Royal Society of Chemistry 2020-12-22 /pmc/articles/PMC9058596/ /pubmed/35516285 http://dx.doi.org/10.1039/d0ra07820d Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Zhumagambetov, Rustam
Kazbek, Daniyar
Shakipov, Mansur
Maksut, Daulet
Peshkov, Vsevolod A.
Fazli, Siamac
cheML.io: an online database of ML-generated molecules
title cheML.io: an online database of ML-generated molecules
title_full cheML.io: an online database of ML-generated molecules
title_fullStr cheML.io: an online database of ML-generated molecules
title_full_unstemmed cheML.io: an online database of ML-generated molecules
title_short cheML.io: an online database of ML-generated molecules
title_sort cheml.io: an online database of ml-generated molecules
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058596/
https://www.ncbi.nlm.nih.gov/pubmed/35516285
http://dx.doi.org/10.1039/d0ra07820d
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