Pruned Machine Learning Models to Predict Aqueous Solubility

[Image: see text] Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous s...

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Detalles Bibliográficos
Autores principales: Perryman, Alexander L., Inoyama, Daigo, Patel, Jimmy S., Ekins, Sean, Freundlich, Joel S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7364544/
https://www.ncbi.nlm.nih.gov/pubmed/32685821
http://dx.doi.org/10.1021/acsomega.0c01251

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7364544/
https://www.ncbi.nlm.nih.gov/pubmed/32685821
http://dx.doi.org/10.1021/acsomega.0c01251

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