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Use of Self-Training Artificial Neural Networks in a QSRR Study of a Diverse Set of Organic Compounds

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Abstract

For a very diverse set of toxicologically compounds, the gas chromatographic Kovats retention indices have been modeled using chemometric methods. First, a genetic algorithm–multiple linear regression (GA–MLR) model has been obtained using molecular descriptors. Then, 15 selected descriptors in the GA–MLR model have been used as input for a self-training artificial neural network (STANN). STANN has been developed as a faster and more accurate non-linear method in our laboratory. After optimization, a 15-9-1 STANN was generated for prediction of retention indices of these organic compounds. The predictive quality of the STANN model was tested for an external prediction set and also five leave-multiple-outs cross-validation sets. Obtained results showed the ability of developed STANN model for predicting retention indices of various compounds. Also, obtained results indicate that in this QSRR study, genetic algorithm is a suitable method for selecting the molecular descriptors.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran

    Zahra Garkani-Nejad

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  1. Zahra Garkani-Nejad
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Garkani-Nejad, Z. Use of Self-Training Artificial Neural Networks in a QSRR Study of a Diverse Set of Organic Compounds. Chroma 70, 869–874 (2009). https://doi.org/10.1365/s10337-009-1241-6

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  • DOI: https://doi.org/10.1365/s10337-009-1241-6

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