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Combining generative artificial intelligence and on-chip synthesis for de novo drug design
Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline w...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195470/ https://www.ncbi.nlm.nih.gov/pubmed/34117066 http://dx.doi.org/10.1126/sciadv.abg3338 |
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author | Grisoni, Francesca Huisman, Berend J. H. Button, Alexander L. Moret, Michael Atz, Kenneth Merk, Daniel Schneider, Gisbert |
author_facet | Grisoni, Francesca Huisman, Berend J. H. Button, Alexander L. Moret, Michael Atz, Kenneth Merk, Daniel Schneider, Gisbert |
author_sort | Grisoni, Francesca |
collection | PubMed |
description | Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis. |
format | Online Article Text |
id | pubmed-8195470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81954702021-06-24 Combining generative artificial intelligence and on-chip synthesis for de novo drug design Grisoni, Francesca Huisman, Berend J. H. Button, Alexander L. Moret, Michael Atz, Kenneth Merk, Daniel Schneider, Gisbert Sci Adv Research Articles Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis. American Association for the Advancement of Science 2021-06-11 /pmc/articles/PMC8195470/ /pubmed/34117066 http://dx.doi.org/10.1126/sciadv.abg3338 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Research Articles Grisoni, Francesca Huisman, Berend J. H. Button, Alexander L. Moret, Michael Atz, Kenneth Merk, Daniel Schneider, Gisbert Combining generative artificial intelligence and on-chip synthesis for de novo drug design |
title | Combining generative artificial intelligence and on-chip synthesis for de novo drug design |
title_full | Combining generative artificial intelligence and on-chip synthesis for de novo drug design |
title_fullStr | Combining generative artificial intelligence and on-chip synthesis for de novo drug design |
title_full_unstemmed | Combining generative artificial intelligence and on-chip synthesis for de novo drug design |
title_short | Combining generative artificial intelligence and on-chip synthesis for de novo drug design |
title_sort | combining generative artificial intelligence and on-chip synthesis for de novo drug design |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195470/ https://www.ncbi.nlm.nih.gov/pubmed/34117066 http://dx.doi.org/10.1126/sciadv.abg3338 |
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