Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Goel:2015:JCA,
  author =       "Purva Goel and Sanket Bapat and Renu Vyas and 
                 Amruta Tambe and Sanjeev S. Tambe",
  title =        "Genetic programming based quantitative
                 structure-retention relationships for the prediction of
                 Kovats retention indices",
  journal =      "Journal of Chromatography A",
  volume =       "1420",
  pages =        "98--109",
  year =         "2015",
  ISSN =         "0021-9673",
  DOI =          "doi:10.1016/j.chroma.2015.09.086",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0021967315014193",
  abstract =     "The development of quantitative structure-retention
                 relationships (QSRR) aims at constructing an
                 appropriate linear/nonlinear model for the prediction
                 of the retention behaviour (such as Kovats retention
                 index) of a solute on a chromatographic column.
                 Commonly, multi-linear regression and artificial neural
                 networks are used in the QSRR development in the gas
                 chromatography (GC). In this study, an artificial
                 intelligence based data-driven modelling formalism,
                 namely genetic programming (GP), has been introduced
                 for the development of quantitative structure based
                 models predicting Kovats retention indices (KRI). The
                 novelty of the GP formalism is that given an example
                 dataset, it searches and optimizes both the form
                 (structure) and the parameters of an appropriate
                 linear/nonlinear data-fitting model. Thus, it is not
                 necessary to pre-specify the form of the data-fitting
                 model in the GP-based modelling. These models are also
                 less complex, simple to understand, and easy to deploy.
                 The effectiveness of GP in constructing QSRRs has been
                 demonstrated by developing models predicting KRIs of
                 light hydrocarbons (case study-I) and adamantane
                 derivatives (case study-II). In each case study, two-,
                 three- and four-descriptor models have been developed
                 using the KRI data available in the literature. The
                 results of these studies clearly indicate that the
                 GP-based models possess an excellent KRI prediction
                 accuracy and generalization capability. Specifically,
                 the best performing four-descriptor models in both the
                 case studies have yielded high (>0.9) values of the
                 coefficient of determination (R2) and low values of
                 root mean squared error (RMSE) and mean absolute
                 percent error (MAPE) for training, test and validation
                 set data. The characteristic feature of this study is
                 that it introduces a practical and an effective
                 GP-based method for developing QSRRs in gas
                 chromatography that can be gainfully used for
                 developing other types of data-driven models in
                 chromatography science.",
  keywords =     "genetic algorithms, genetic programming, Gas
                 chromatography, Kovats retention index, Quantitative
                 structure-retention relationships, Artificial
                 intelligence, Molecular descriptors",
}

Genetic Programming entries for Purva Goel Sanket Bapat Renu Vyas Amruta Tambe Sanjeev S Tambe

Citations