Cargando…
Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis
Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chron...
Autores principales: | Matsuzaka, Yasunari, Uesawa, Yoshihiro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447703/ https://www.ncbi.nlm.nih.gov/pubmed/30984753 http://dx.doi.org/10.3389/fbioe.2019.00065 |
Ejemplares similares
-
DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance
por: Matsuzaka, Yasunari, et al.
Publicado: (2020) -
Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning
por: Matsuzaka, Yasunari, et al.
Publicado: (2020) -
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) -
Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library
por: Matsuzaka, Yasunari, et al.
Publicado: (2019) -
Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships
por: Matsuzaka, Yasunari, et al.
Publicado: (2023)