Please use this identifier to cite or link to this item:
|Title:||Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties|
|Authors:||Yap, C.W. |
Statistical learning methods
|Source:||Yap, 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. https://doi.org/10.2174/138955707782331696|
|Abstract:||Quantitative 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.|
|Source Title:||Mini-Reviews in Medicinal Chemistry|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2017
WEB OF SCIENCETM
checked on Nov 20, 2017
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.