On the use of PLS and N-PLS in MIA-QSAR: Azole antifungals

被引：34

作者：

Goodarzi, Mohammad ^{[2
,3
]}

Freitas, Matheus P. ^{[1
]}

机构：

[1] Univ Fed Lavras UFLA, Dept Quim, BR-37200000 Lavras, MG, Brazil

[2] Azad Univ, Fac Sci, Dept Chem, Arak, Iran

[3] Azad Univ, Young Researchers Club, Arak, Iran

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2009年 / 96卷 / 01期

关键词：

MIA-QSAR; PLS regression; N-PLS regression; Antifungals; IMAGE-BASED APPROACH; MULTIVARIATE QSAR; MULTILINEAR PLS; DERIVATIVES; SERIES; AGENTS;

D O I：

10.1016/j.chemolab.2008.11.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The antifungal activities of a series of azole derivatives have been modeled by using MIA (multivariate image analysis) descriptors. Two regression methods were applied to correlate such descriptors with the activities column vector: bilinear (classical) and multilinear (N-way) partial least squares-PLS and WKS, respectively. The PLS-based model for this series of compounds demonstrated higher predictive ability than the N-PLS-based model, in opposition to some published results for other series of compounds. The activities block was taken in logarithmic scale (pMiC(90(cpd))/pMIC(90(bifonazole))) and the statistical performance of both models was found to be significantly better than the CoMFA analysis previously established. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：59 / 62

页数：4

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