Improved Prediction of Blood–Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints-Reference-Cited by-同舟云学术

Improved Prediction of Blood–Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints

Published:2018-03-21 Issue:3 Volume:20 Page:
ISSN:1550-7416
Container-title:The AAPS Journal
language:en
Short-container-title:AAPS J

Author:

Yuan Yaxia,Zheng Fang,Zhan Chang-Guo

Publisher

Springer Science and Business Media LLC

Subject

Pharmaceutical Science

Link

http://link.springer.com/article/10.1208/s12248-018-0215-8/fulltext.html

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3. Bicker J, Alves G, Fortuna A, Falcão A. Blood–brain barrier models and their relevance for a successful development of CNS drug delivery systems: a review. Eur J Pharm Biopharm. 2014;87(3):409–32.

4. Abraham MH. The factors that influence permeation across the blood–brain barrier. Eur J Med Chem. 2004;39(3):235–40.

5. Rose K, Hall LH, Kier LB. Modeling blood-brain barrier partitioning using the electrotopological state. J Chem Inf Comput Sci. 2002;42(3):651–66.

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