Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of re...

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Detalles Bibliográficos
Autores principales: García-Sosa, Alfonso T., Maran, Uko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267747/
https://www.ncbi.nlm.nih.gov/pubmed/34206613
http://dx.doi.org/10.3390/ijms22136695

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267747/
https://www.ncbi.nlm.nih.gov/pubmed/34206613
http://dx.doi.org/10.3390/ijms22136695

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