A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models-Reference-Cited by-同舟云学术

A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models

Published:2006-09-01 Issue:5 Volume:46 Page:1984-1995
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zhang Shuxing¹,Golbraikh Alexander¹,Oloff Scott¹,Kohn Harold¹,Tropsha Alexander¹

Affiliation:

1. Division of Medicinal Chemistry and Natural Products, School of Pharmacy, CB # 7360 Beard Hall, University of North Carolina, Chapel Hill, North Carolina 27599

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/ci060132x

Reference57 articles.

1. Quantitative structure-selectivity relationships. Comparison of the inhibition of Escherichia coli and bovine liver dihydrofolate reductase by 5-(substituted benzyl)-2,4-diaminopyrimidines

2. The Correlation of Biological Activity of Plant Growth Regulators and Chloromycetin Derivatives with Hammett Constants and Partition Coefficients

3. Chem-Bioinformatics and QSAR: A Review of QSAR Lacking Positive Hydrophobic Terms

4. QSAR and ADME

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