Application of GFA-MLR and G/PLS Techniques in QSAR/QSPR Studies with Application in Medicinal Chemistry and Predictive Toxicology

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@InCollection{Roy:2015:hbgpa,
  author =       "Partha Pratim Roy and Supratim Ray and Kunal Roy",
  title =        "Application of GFA-MLR and G/PLS Techniques in
                 QSAR/QSPR Studies with Application in Medicinal
                 Chemistry and Predictive Toxicology",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "20",
  pages =        "501--529",
  keywords =     "genetic algorithms, genetic programming, QSAR, QSPR,
                 QSTR, MARS, GFA, G/PLS, Predictive toxicology,
                 Medicinal chemistry",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_20",
  abstract =     "Quantitative structure-activity/property/toxicity
                 relationship (QSAR/QSPR/QSTR) models enable predictions
                 of activity/property/toxicities to be made directly
                 from the chemical structure. Feature selection is one
                 of the integral parts in the development of QSAR/QSPR
                 models which is also included in the Organization of
                 Economic Co-operation and Development (OECD) principle
                 of an unambiguous algorithm for QSAR model development
                 and validation. Genetic algorithm (GA) based on the
                 principle of Darwin's theory of natural selection and
                 evolutions are being widely used in recent times for
                 the selection of descriptors in the development of
                 predictive models for toxicity assessment and virtual
                 screening of hazardous chemicals and design of drug
                 compounds with therapeutic activity. The GA algorithm
                 can handle a huge number of descriptors and generate a
                 population of models competitive with or superior to
                 the results of standard regression analysis. Genetic
                 function approximation (GFA) involves the combination
                 of multivariate adaptive regression splines (MARS)
                 algorithm of Friedman with genetic algorithm of Holland
                 to evolve population of equations. GFA calculations are
                 based on three operators: selection, crossover and
                 mutation. Using spline based terms in the model
                 construction, GFA can either remove the outlier
                 compounds or identify a range of effect. GFA followed
                 by multiple linear regression (GFA-MLR) or partial
                 least squares (G/PLS) regression is frequently used by
                 different research groups for the development of
                 predictive QSAR/QSPR models. This chapter presents
                 examples of some case studies of the use of GFA-MLR and
                 G/PLS techniques in developing predictive models in
                 medicinal chemistry and predictive toxicology
                 applications.",
}

Genetic Programming entries for Partha Pratim Roy Supratim Ray Kunal Roy

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