Current Mathematical Methods Used in QSAR/QSPR Studies

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "Current Mathematical Methods Used in {QSAR}/{QSPR}
  author =       "Peixun Liu and Wei Long",
  journal =      "International Journal of Molecular Sciences",
  publisher =    "Molecular Diversity Preservation International",
  year =         "2009",
  ISSN =         "1422-0067; 14220067",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  oai =          "oai:doaj-articles:a634e99c5db7a3846db7d582ee717285",
  keywords =     "genetic algorithms, genetic programming, QSAR, QSPR,
                 Mathematical methods, Regression, Algorithm",
  URL =          "",
  DOI =          "doi:10.3390/ijms10051978",
  URL =          "",
  broken =       "\&genre=article\&issn=14220067\&date=2009\&volume=10\&issue=5\&spage=1978",
  size =         "21 pages",
  abstract =     "This paper gives an overview of the mathematical
                 methods currently used in quantitative
                 structure-activity/property relationship (QASR/QSPR)
                 studies. Recently, the mathematical methods applied to
                 the regression of QASR/QSPR models are developing very
                 fast, and new methods, such as Gene Expression
                 Programming (GEP), Project Pursuit Regression (PPR) and
                 Local Lazy Regression (LLR) have appeared on the
                 QASR/QSPR stage. At the same time, the earlier methods,
                 including Multiple Linear Regression (MLR), Partial
                 Least Squares (PLS), Neural Networks (NN), Support
                 Vector Machine (SVM) and so on, are being upgraded to
                 improve their performance in QASR/QSPR studies. These
                 new and upgraded methods and algorithms are described
                 in detail, and their advantages and disadvantages are
                 evaluated and discussed, to show their application
                 potential in QASR/QSPR studies in the future.",

Genetic Programming entries for Peixun Liu Wei Long