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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecul...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941972/ https://www.ncbi.nlm.nih.gov/pubmed/31900408 http://dx.doi.org/10.1038/s41467-019-13807-w |
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author | Méndez-Lucio, Oscar Baillif, Benoit Clevert, Djork-Arné Rouquié, David Wichard, Joerg |
author_facet | Méndez-Lucio, Oscar Baillif, Benoit Clevert, Djork-Arné Rouquié, David Wichard, Joerg |
author_sort | Méndez-Lucio, Oscar |
collection | PubMed |
description | Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery. |
format | Online Article Text |
id | pubmed-6941972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69419722020-01-06 De novo generation of hit-like molecules from gene expression signatures using artificial intelligence Méndez-Lucio, Oscar Baillif, Benoit Clevert, Djork-Arné Rouquié, David Wichard, Joerg Nat Commun Article Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery. Nature Publishing Group UK 2020-01-03 /pmc/articles/PMC6941972/ /pubmed/31900408 http://dx.doi.org/10.1038/s41467-019-13807-w Text en © The Author(s) 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/. |
spellingShingle | Article Méndez-Lucio, Oscar Baillif, Benoit Clevert, Djork-Arné Rouquié, David Wichard, Joerg De novo generation of hit-like molecules from gene expression signatures using artificial intelligence |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941972/ https://www.ncbi.nlm.nih.gov/pubmed/31900408 http://dx.doi.org/10.1038/s41467-019-13807-w |
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