Current Mathematical Methods Used in QSAR/QSPR Studies

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Liu:2009:IJMS,

• title = "Current Mathematical Methods Used in {QSAR}/{QSPR} Studies",
• 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 www.doaj.org",
• language = "eng",
• oai = "oai:doaj-articles:a634e99c5db7a3846db7d582ee717285",
• keywords = "genetic algorithms, genetic programming, QSAR, QSPR, Mathematical methods, Regression, Algorithm",
• URL = "http://www.mdpi.com/1422-0067/10/5/1978/pdf",
• DOI = "doi:10.3390/ijms10051978",
• URL = "http://www.mdpi.com/1422-0067/10/5/1978/",
• broken = "http://www.doaj.org/doaj?func=openurl\&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

Citations