Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods-Reference-Cited by-同舟云学术

Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods

Published:2005-08-10 Issue:5 Volume:45 Page:1376-1384
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Li Hu¹,Yap Chun Wei¹,Ung Choong Yong¹,Xue Ying¹,Cao Zhi Wei¹,Chen Yu Zong¹

Affiliation:

1. Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543, College of Chemistry, Sichuan University, Chengdu 610064, P. R. China, and Shanghai Center for Bioinformation Technology, Shanghai 201203, P. R. China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci050135u

Reference66 articles.

1. ADME/PK as part of a rational approach to drug discovery

2. Predicting ADME properties in silico: methods and models

3. Computational approaches to the prediction of the blood–brain distribution

4. Early ADME/Tox studies and in silico screening

5. Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists

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