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

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 on

SUBMITTER: Mendez-Lucio O 

PROVIDER: S-EPMC6941972 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature