Improving QSAR models for the biological activity of HIV Reverse Transcriptase inhibitors: Aspects of outlier detection and uninformative variable elimination

M Daszykowski; I Stanimirova; B Walczak; F Daeyaert; M R de Jonge; J Heeres; L M H Koymans; P J Lewi; H M Vinkers; P A Janssen; D L Massart

doi:10.1016/j.talanta.2005.04.071

Improving QSAR models for the biological activity of HIV Reverse Transcriptase inhibitors: Aspects of outlier detection and uninformative variable elimination

Talanta. 2005 Nov 15;68(1):54-60. doi: 10.1016/j.talanta.2005.04.071. Epub 2005 Jun 17.

Authors

M Daszykowski¹, I Stanimirova, B Walczak, F Daeyaert, M R de Jonge, J Heeres, L M H Koymans, P J Lewi, H M Vinkers, P A Janssen, D L Massart

Affiliation

¹ FABI, ChemoAC, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium; Department of Chemometrics, Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland.

PMID: 18970284
DOI: 10.1016/j.talanta.2005.04.071

Abstract

The goal of this study is to derive a methodology for modeling the biological activity of non-nucleoside HIV Reverse Transcriptase (RT) inhibitors. The difficulties that were encountered during the modeling attempts are discussed, together with their origin and solutions. With the selected multivariate techniques: robust principal component analysis, partial least squares, robust partial least squares and uninformative variable elimination partial least squares, it is possible to explore and to model the contaminated data satisfactory. It is shown that these techniques are versatile and valuable tools in modeling and exploring biochemical data.