Optimized block-wise variable combination by particle swarm optimization for partial least squares modeling in quantitative structure-activity relationship studies.
The use of numerous descriptors that are indicative of molecular structure is becoming common in quantitative structure-activity relationship (QSAR) studies. As all of the descriptors might carry more or less molecular information, it seems more advisable to investigate the possible variable combination rather than variable selection. In this paper, an optimized block-wise variable combination (OBVC) by particle swarm optimization based on partial least squares modeling has been proposed for variable combination. An F statistic is also introduced to determine the dimensionality of the PLS model. The performance is assessed using two QSAR data sets. Experimental results have shown the good performance of this technique compared to those obtained by stepwise regression.
Lin
Wei-Qi
WQ
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Jiang
Jian-Hui
JH
Shen
Qi
Q
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D000588:Amines / Q000737:chemistry; D000804:Angiotensin II / Q000037:antagonists & inhibitors / Q000378:metabolism; D016018:Least-Squares Analysis; D008956:Models, Chemical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D014230:Triazoles / Q000737:chemistry / Q000494:pharmacology
D000588:Amines; D014230:Triazoles; D000804:Angiotensin II; C045575:1,2,4-triazole