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Current Mathematical Methods Used in QSAR/QSPR Studies
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 Pr...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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Molecular Diversity Preservation International (MDPI)
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2695261/ https://www.ncbi.nlm.nih.gov/pubmed/19564933 http://dx.doi.org/10.3390/ijms10051978 |
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author | Liu, Peixun Long, Wei |
author_facet | Liu, Peixun Long, Wei |
author_sort | Liu, Peixun |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2695261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-26952612009-06-29 Current Mathematical Methods Used in QSAR/QSPR Studies Liu, Peixun Long, Wei Int J Mol Sci Review 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. Molecular Diversity Preservation International (MDPI) 2009-04-29 /pmc/articles/PMC2695261/ /pubmed/19564933 http://dx.doi.org/10.3390/ijms10051978 Text en © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Review Liu, Peixun Long, Wei Current Mathematical Methods Used in QSAR/QSPR Studies |
title | Current Mathematical Methods Used in QSAR/QSPR Studies |
title_full | Current Mathematical Methods Used in QSAR/QSPR Studies |
title_fullStr | Current Mathematical Methods Used in QSAR/QSPR Studies |
title_full_unstemmed | Current Mathematical Methods Used in QSAR/QSPR Studies |
title_short | Current Mathematical Methods Used in QSAR/QSPR Studies |
title_sort | current mathematical methods used in qsar/qspr studies |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2695261/ https://www.ncbi.nlm.nih.gov/pubmed/19564933 http://dx.doi.org/10.3390/ijms10051978 |
work_keys_str_mv | AT liupeixun currentmathematicalmethodsusedinqsarqsprstudies AT longwei currentmathematicalmethodsusedinqsarqsprstudies |