Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists

Qi Shen; Jian-Hui Jiang; Chen-Xu Jiao; Guo-Li Shen; Ru-Qin Yu

doi:10.1016/j.ejps.2004.03.002

Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists

Eur J Pharm Sci. 2004 Jun;22(2-3):145-52. doi: 10.1016/j.ejps.2004.03.002.

Authors

Qi Shen¹, Jian-Hui Jiang, Chen-Xu Jiao, Guo-Li Shen, Ru-Qin Yu

Affiliation

¹ State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.

PMID: 15158899
DOI: 10.1016/j.ejps.2004.03.002

Abstract

A version of modified particle swarm optimization (PSO) algorithm has been proposed. The PSO algorithm has been modified to adopt to the discrete combinatorial optimization problem and reduce the probability of sinking into local optima. In the modified PSO algorithm, the velocity represents the probability of element in each particle taking value 1 or 0. The modified discrete PSO algorithm is proposed to select variables in MLR and PLS modeling and to predict antagonism of angiotensin II antagonists. The modified C(p) is employed as fitness function. The results were compared to those obtained by GAs. Experimental results have demonstrated that the modified PSO is a useful tool for variable selection which converges quickly towards the optimal position.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Angiotensin II / antagonists & inhibitors*
Angiotensin II / chemistry*
Least-Squares Analysis
Linear Models
Models, Chemical*
Particle Size
Quantitative Structure-Activity Relationship*

Substances

Angiotensin II