De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

High quality hit identification remains a considerable challenge in de novo drug design. Here, the authors train a generative adversarial network with transcriptome profiles induced by a large set of compounds, enabling it to design molecules that are likely to induce desired expression profiles.

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Autores principales:	Oscar Méndez-Lucio, Benoit Baillif, Djork-Arné Clevert, David Rouquié, Joerg Wichard
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/8f29fb81be1743d6927814a8642026b4
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spelling	oai:doaj.org-article:8f29fb81be1743d6927814a8642026b42021-12-02T15:39:10ZDe novo generation of hit-like molecules from gene expression signatures using artificial intelligence10.1038/s41467-019-13807-w2041-1723https://doaj.org/article/8f29fb81be1743d6927814a8642026b42020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13807-whttps://doaj.org/toc/2041-1723High quality hit identification remains a considerable challenge in de novo drug design. Here, the authors train a generative adversarial network with transcriptome profiles induced by a large set of compounds, enabling it to design molecules that are likely to induce desired expression profiles.Oscar Méndez-LucioBenoit BaillifDjork-Arné ClevertDavid RouquiéJoerg WichardNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Oscar Méndez-Lucio Benoit Baillif Djork-Arné Clevert David Rouquié Joerg Wichard De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
description	High quality hit identification remains a considerable challenge in de novo drug design. Here, the authors train a generative adversarial network with transcriptome profiles induced by a large set of compounds, enabling it to design molecules that are likely to induce desired expression profiles.
format	article
author	Oscar Méndez-Lucio Benoit Baillif Djork-Arné Clevert David Rouquié Joerg Wichard
author_facet	Oscar Méndez-Lucio Benoit Baillif Djork-Arné Clevert David Rouquié Joerg Wichard
author_sort	Oscar Méndez-Lucio
title	De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
title_short	De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
title_full	De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
title_fullStr	De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
title_full_unstemmed	De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
title_sort	de novo generation of hit-like molecules from gene expression signatures using artificial intelligence
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8f29fb81be1743d6927814a8642026b4
work_keys_str_mv	AT oscarmendezlucio denovogenerationofhitlikemoleculesfromgeneexpressionsignaturesusingartificialintelligence AT benoitbaillif denovogenerationofhitlikemoleculesfromgeneexpressionsignaturesusingartificialintelligence AT djorkarneclevert denovogenerationofhitlikemoleculesfromgeneexpressionsignaturesusingartificialintelligence AT davidrouquie denovogenerationofhitlikemoleculesfromgeneexpressionsignaturesusingartificialintelligence AT joergwichard denovogenerationofhitlikemoleculesfromgeneexpressionsignaturesusingartificialintelligence
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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

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