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Application of modified particle swarm optimization as an efficient variable selection strategy in QSAR/QSPR studies


Hamid Modarress, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue,15914 Tehran, Iran.



In this work, we present a novel method combining modified particle swarm optimization (MPSO) and partial least squares (PLS) for selecting a subset of relevant descriptors and building the optimal linear model for quantitative structure–activity relationship (QSAR)/quantitative structure–property relationship (QSPR) studies. The proposed method is applied to the QSPR modeling of the entropy of formation for 1133 different organic compounds. By using the MPSO–PLS approach, three descriptors are selected from a large set of molecular descriptors, and a linear model is developed. Analysis of the results indicates the squared correlation coefficient of 0.9691 for MPSO–PLS model. Thus, MPSO–PLS is a promising technique, which is helpful for developing the QSAR/QSPR models with high accuracy. Copyright © 2012 John Wiley & Sons, Ltd.