Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders.
adversarial autoencoders
conditional generation
deep learning
drug discovery
gene expression
generative models
representation learning
Journal
Frontiers in pharmacology
ISSN: 1663-9812
Titre abrégé: Front Pharmacol
Pays: Switzerland
ID NLM: 101548923
Informations de publication
Date de publication:
2020
2020
Historique:
received:
11
11
2019
accepted:
25
02
2020
entrez:
5
5
2020
pubmed:
5
5
2020
medline:
5
5
2020
Statut:
epublish
Résumé
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
Identifiants
pubmed: 32362822
doi: 10.3389/fphar.2020.00269
pmc: PMC7182000
doi:
Types de publication
Journal Article
Langues
eng
Pagination
269Informations de copyright
Copyright © 2020 Shayakhmetov, Kuznetsov, Zhebrak, Kadurin, Nikolenko, Aliper and Polykovskiy.
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