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Current Mathematical Methods Used in QSAR/QSPR Studies

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Author to whom correspondence should be addressed.
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
Int. J. Mol. Sci. 2009, 10(5), 1978-1998;
Received: 19 March 2009 / Accepted: 28 April 2009 / Published: 29 April 2009
(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)
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. View Full-Text
Keywords: QSAR; QSPR; Mathematical methods; Regression; Algorithm QSAR; QSPR; Mathematical methods; Regression; Algorithm
MDPI and ACS Style

Liu, P.; Long, W. Current Mathematical Methods Used in QSAR/QSPR Studies. Int. J. Mol. Sci. 2009, 10, 1978-1998.

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