TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning.


Journal

Journal of chemical information and modeling
ISSN: 1549-960X
Titre abrégé: J Chem Inf Model
Pays: United States
ID NLM: 101230060

Informations de publication

Date de publication:
27 09 2021
Historique:
pubmed: 17 9 2021
medline: 25 2 2023
entrez: 16 9 2021
Statut: ppublish

Résumé

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based

Identifiants

pubmed: 34528432
doi: 10.1021/acs.jcim.1c00967
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4303-4320

Auteurs

Kazuma Kaitoh (K)

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.

Yoshihiro Yamanishi (Y)

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.

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Classifications MeSH