A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model.
machine learning
MIEC-SVM model
androgen receptor antagonist
prostate cancer
virtual screening
Journal
Acta pharmacologica Sinica
ISSN: 1745-7254
Titre abrégé: Acta Pharmacol Sin
Pays: United States
ID NLM: 100956087
Informations de publication
Date de publication:
15 May 2024
15 May 2024
Historique:
received:
10
01
2024
accepted:
03
04
2024
medline:
16
5
2024
pubmed:
16
5
2024
entrez:
15
5
2024
Statut:
aheadofprint
Résumé
Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC
Identifiants
pubmed: 38750073
doi: 10.1038/s41401-024-01284-x
pii: 10.1038/s41401-024-01284-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Shanghai Institute of Materia Medica, Chinese Academy of Sciences and Chinese Pharmacological Society.
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