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Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders by Shayakhmetov, Rim; Kuznetsov, Maksim; Zhebrak, Alexander; Kadurin, Artur; Nikolenko, Sergey; Aliper, Alexander; Polykovskiy, Daniil is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders about?

Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model—the Bidirectional Adversarial Autoencoder—explicitly separates cellular processes captured in gene expression changes into two feature sets: those __related__ and __unrelated__ to the drug incubation. The model uses __related__ features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE.

Who reads Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders?

Author

Shayakhmetov, Rim; Kuznetsov, Maksim; Zhebrak, Alexander; Kadurin, Artur; Nikolenko, Sergey; Aliper, Alexander; Polykovskiy, Daniil

Publisher

Frontiers; Frontiers Media SA; Frontiers Media S.A.; Lausanne: Frontiers Media S.A., 2010- (ISSN 1663-9812)

Published

2020

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)