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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...

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Detalles Bibliográficos
Autores principales: Liu, Peixun, Long, Wei
Formato: Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2009
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.
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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
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