Cargando…
Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct...
Autores principales: | Matsuzaka, Yasunari, Totoki, Shin, Handa, Kentaro, Shiota, Tetsuyoshi, Kurosaki, Kota, Uesawa, Yoshihiro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509615/ https://www.ncbi.nlm.nih.gov/pubmed/34639159 http://dx.doi.org/10.3390/ijms221910821 |
Ejemplares similares
-
A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures
por: Kurosaki, Kota, et al.
Publicado: (2020) -
A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance
por: Matsuzaka, Yasunari, et al.
Publicado: (2022) -
Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library
por: Matsuzaka, Yasunari, et al.
Publicado: (2020) -
DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance
por: Matsuzaka, Yasunari, et al.
Publicado: (2020) -
Molecular Initiating Events Associated with Drug-Induced Liver Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity Predictions
por: Kurosaki, Kota, et al.
Publicado: (2021)