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Nat Commun. 2020 Jan 3;11(1):10. doi: 10.1038/s41467-019-13807-w.

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

Author information

1
Bayer SAS, Bayer Crop Science, 355 rue Dostoïevski, CS 90153, 06906, Valbonne, Sophia Antipolis Cedex, France. oscar.mendezlucio.ext@bayer.com.
2
Bloomoon, 13 Avenue Albert Einstein, 69100, Villeurbanne, France. oscar.mendezlucio.ext@bayer.com.
3
Bayer SAS, Bayer Crop Science, 355 rue Dostoïevski, CS 90153, 06906, Valbonne, Sophia Antipolis Cedex, France.
4
Department of Machine Learning Research, Bayer AG, 13353, Berlin, Germany.
5
Bayer SAS, Bayer Crop Science, 355 rue Dostoïevski, CS 90153, 06906, Valbonne, Sophia Antipolis Cedex, France. david.rouquie@bayer.com.
6
Department of Genetic Toxicology, Bayer AG, 13353, Berlin, Germany. joerg.wichard@bayer.com.

Abstract

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.

PMID:
31900408
PMCID:
PMC6941972
DOI:
10.1038/s41467-019-13807-w
[Indexed for MEDLINE]
Free PMC Article

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