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Modified particle swarm optimization method for variable selection in QSAR/QSPR studies

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Abstract

The selection of the most relevant variables is an important step in the QSAR/QSPR modeling process. In this work we apply modified particle swarm optimization (MPSO) based on multiple linear regression (MLR) for selecting a small subset of descriptors that has significant contribution to the Gibbs energy of formation for a diverse set of organic compounds. Nonlinear relationships between selected molecular descriptors and Gibbs energy of formation are achieved by radial basis function neural network (RBF NN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) methods. The MLR, RBF NN, ANFIS, and SVM squared correlation coefficients are 0.928, 0.946, 0.945, and 0.947, respectively. The obtained results suggest that the proposed MPSO is an efficient and powerful method for feature selection (descriptor selection) in the QSAR/QSPR studies.

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Authors and Affiliations

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

    Aboozar Khajeh & Hamid Modarress

  2. Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue, 15914, Tehran, Iran

    Hamed Zeinoddini-Meymand

Authors
  1. Aboozar Khajeh
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  2. Hamid Modarress
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  3. Hamed Zeinoddini-Meymand
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Correspondence to Hamid Modarress.

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Khajeh, A., Modarress, H. & Zeinoddini-Meymand, H. Modified particle swarm optimization method for variable selection in QSAR/QSPR studies. Struct Chem 24, 1401–1409 (2013). https://doi.org/10.1007/s11224-012-0165-1

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