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De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

preprint
submitted on 03.11.2018 and posted on 05.11.2018 by Oscar Méndez-Lucio, Benoit Baillif, Djork-Arné Clevert, David Rouquié, Joerg Wichard
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 the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

History

Email Address of Submitting Author

oscar.mendezlucio.ext@bayer.com

Institution

Bayer CropScience

Country

France

ORCID For Submitting Author

0000-0003-0345-1168

Declaration of Conflict of Interest

no conflict of interest

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