Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important ca...

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Detalles Bibliográficos
Autores principales: Yang, Yilong, Ye, Zhuyifan, Su, Yan, Zhao, Qianqian, Li, Xiaoshan, Ouyang, Defang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Original article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362259/
https://www.ncbi.nlm.nih.gov/pubmed/30766789
http://dx.doi.org/10.1016/j.apsb.2018.09.010

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362259/
https://www.ncbi.nlm.nih.gov/pubmed/30766789
http://dx.doi.org/10.1016/j.apsb.2018.09.010

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