Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/53133
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dc.titleRegression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties
dc.contributor.authorYap, C.W.
dc.contributor.authorLi, H.
dc.contributor.authorJi, Z.L.
dc.contributor.authorChen, Y.Z.
dc.date.accessioned2014-05-19T02:54:39Z
dc.date.available2014-05-19T02:54:39Z
dc.date.issued2007-11
dc.identifier.citationYap, C.W., Li, H., Ji, Z.L., Chen, Y.Z. (2007-11). Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties. Mini-Reviews in Medicinal Chemistry 7 (11) : 1097-1107. ScholarBank@NUS Repository.
dc.identifier.issn13895575
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53133
dc.description.abstractQuantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models have been extensively used for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property from structure-derived physicochemical and structural features. These models can be developed by using various regression methods including conventional approaches (multiple linear regression and partial least squares) and more recently explored genetic (genetic function approximation) and machine learning (k-nearest neighbour, neural networks, and support vector regression) approaches. This article describes the algorithm of these methods, evaluates their advantages and disadvantages, and discusses the application potential of did recently explored methods. Freely available online and commercial software for these regression methods and the areas of their applications are also presented. © 2007 Bentham Science Publishers Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.2174/138955707782331696
dc.sourceScopus
dc.subjectADME
dc.subjectADMET
dc.subjectCompound
dc.subjectDrug
dc.subjectPharmacodynamics
dc.subjectPharmacokinetics
dc.subjectQSAR
dc.subjectQSPR
dc.subjectStatistical learning methods
dc.subjectStructure-activity relationship
dc.subjectToxicity
dc.typeArticle
dc.contributor.departmentPHARMACY
dc.description.sourcetitleMini-Reviews in Medicinal Chemistry
dc.description.volume7
dc.description.issue11
dc.description.page1097-1107
dc.description.codenMMCIA
dc.identifier.isiut000250698700002
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