Replacement Method and Enhanced Replacement Method Versus the Genetic Algorithm Approach for the Selection of Molecular Descriptors in QSPR/QSAR Theories

被引：74

作者：

Mercader, Andrew G. ^{[1
,2
]}

Duchowicz, Pablo R. ^{[1
]}

Fernandez, Francisco M. ^{[1
]}

Castro, Eduardo A. ^{[1
]}

机构：

[1] UNLP, INIFTA, CCT La Plata, CONICET, RA-1900 La Plata, Argentina

[2] Univ Buenos Aires, Fac Farm & Bioquim, PRALIB, CONICET, RA-1113 Buenos Aires, DF, Argentina

来源：

JOURNAL OF CHEMICAL INFORMATION AND MODELING | 2010年 / 50卷 / 09期

关键词：

VARIABLE SELECTION; QSAR; QSPR; TOXICITY; PREDICTION; OPTIMIZATION; SEARCH; MODELS;

D O I：

10.1021/ci100103r

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.

引用

页码：1542 / 1548

页数：7

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