The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry

[Image: see text] This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical ass...

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Autores principales: Ivanenkov, Yan, Zagribelnyy, Bogdan, Malyshev, Alex, Evteev, Sergei, Terentiev, Victor, Kamya, Petrina, Bezrukov, Dmitry, Aliper, Alex, Ren, Feng, Zhavoronkov, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351082/
https://www.ncbi.nlm.nih.gov/pubmed/37465301
http://dx.doi.org/10.1021/acsmedchemlett.3c00041
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author Ivanenkov, Yan
Zagribelnyy, Bogdan
Malyshev, Alex
Evteev, Sergei
Terentiev, Victor
Kamya, Petrina
Bezrukov, Dmitry
Aliper, Alex
Ren, Feng
Zhavoronkov, Alex
author_facet Ivanenkov, Yan
Zagribelnyy, Bogdan
Malyshev, Alex
Evteev, Sergei
Terentiev, Victor
Kamya, Petrina
Bezrukov, Dmitry
Aliper, Alex
Ren, Feng
Zhavoronkov, Alex
author_sort Ivanenkov, Yan
collection PubMed
description [Image: see text] This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures’ relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.
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spelling pubmed-103510822023-07-18 The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry Ivanenkov, Yan Zagribelnyy, Bogdan Malyshev, Alex Evteev, Sergei Terentiev, Victor Kamya, Petrina Bezrukov, Dmitry Aliper, Alex Ren, Feng Zhavoronkov, Alex ACS Med Chem Lett [Image: see text] This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures’ relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD. American Chemical Society 2023-06-30 /pmc/articles/PMC10351082/ /pubmed/37465301 http://dx.doi.org/10.1021/acsmedchemlett.3c00041 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Ivanenkov, Yan
Zagribelnyy, Bogdan
Malyshev, Alex
Evteev, Sergei
Terentiev, Victor
Kamya, Petrina
Bezrukov, Dmitry
Aliper, Alex
Ren, Feng
Zhavoronkov, Alex
The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry
title The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry
title_full The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry
title_fullStr The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry
title_full_unstemmed The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry
title_short The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry
title_sort hitchhiker’s guide to deep learning driven generative chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351082/
https://www.ncbi.nlm.nih.gov/pubmed/37465301
http://dx.doi.org/10.1021/acsmedchemlett.3c00041
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