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Int. J. Mol. Sci. 2009, 10(5), 1978-1998; doi:10.3390/ijms10051978
Review

Current Mathematical Methods Used in QSAR/QSPR Studies

 and
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)
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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 QSAR; QSPR; Mathematical methods; Regression; Algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Liu P, Long W. Current Mathematical Methods Used in QSAR/QSPR Studies. International Journal of Molecular Sciences. 2009; 10(5):1978-1998.

Chicago/Turabian Style

Liu, Peixun; Long, Wei. 2009. "Current Mathematical Methods Used in QSAR/QSPR Studies." Int. J. Mol. Sci. 10, no. 5: 1978-1998.



Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert