A Model-Based Ensembling Approach for Developing QSARs-Reference-Cited by-同舟云学术

A Model-Based Ensembling Approach for Developing QSARs

Published:2009-07-13 Issue:8 Volume:49 Page:1857-1865
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zhang Qianyi¹,Hughes-Oliver Jacqueline M.¹,Ng Raymond T.¹

Affiliation:

1. Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, and Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

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/ci900080f

Reference23 articles.

1. On the significance of clusters in the graphical display of structure-activity data

2. Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning

3. QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors

4. Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms

5. Neural networks applied to pharmaceutical problems. III. Neural networks applied to quantitative structure-activity relationship (QSAR) analysis

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