Current Computer-Aided Drug Design

Subhash C. Basak
Departments of Chemistry, Biochemistry & Molecular Biology University of Minnesota Duluth
Duluth, MN 55811
USA

Back

Variable Subset Selection in the Presence of Flagged Observations and Multicollinear Descriptors in QSAR

Author(s): Peter P. Mager, Luis Sanchez.

Abstract:

A major problem in traditional quantitative structure-activity relationships (QSARs) analysis is to select suitable chemical descriptors from a large pool of variables. Decisions against or in favor of a particular descriptor depends entirely on the result of statistically based hypothesis testing. Uncertain results may be produced in presence of multicollinear descriptors and flagged observations (high-leverage points, outliers, influential data). To satisfy the assumptions for hypothesis testing, diagnostic statistics and subsequent design repair are employed. Here we show an example with nonnucleoside HIV-1 reverse transcriptase inhibitors.

Keywords: computer-assisted drug design, quantitative structure-activity relationships, regression analysis, hypothesis testing, design repair, diagnostic statistics, artificial neural networks, nonnucleoside hiv-1 reverse, transcriptase inhibitors

Order Reprints Order Eprints Rights & PermissionsPrintExport

Article Details

VOLUME: 1
ISSUE: 2
Year: 2005
Page: [163 - 177]
Pages: 15
DOI: 10.2174/1573409053585654
Price: $58