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Current Mathematical Methods Used in QSAR/QSPR Studies
Institute of Radiation Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Tianjin 300192, P.R. China
These authors contributed equally to this work
* Author to whom correspondence should be addressed.
Received: 19 March 2009; in revised form: / Accepted: 28 April 2009 / Published: 29 April 2009
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.
Keywords: QSAR; QSPR; Mathematical methods; Regression; Algorithm
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MDPI and ACS Style
Liu, P.; Long, W. Current Mathematical Methods Used in QSAR/QSPR Studies. Int. J. Mol. Sci. 2009, 10, 1978-1998.
Liu P, Long W. Current Mathematical Methods Used in QSAR/QSPR Studies. International Journal of Molecular Sciences. 2009; 10(5):1978-1998.
Liu, Peixun; Long, Wei. 2009. "Current Mathematical Methods Used in QSAR/QSPR Studies." Int. J. Mol. Sci. 10, no. 5: 1978-1998.