Please use this identifier to cite or link to this item: https://doi.org/10.2174/138955707782331696
Title: Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties
Authors: Yap, C.W. 
Li, H. 
Ji, Z.L.
Chen, Y.Z. 
Keywords: ADME
ADMET
Compound
Drug
Pharmacodynamics
Pharmacokinetics
QSAR
QSPR
Statistical learning methods
Structure-activity relationship
Toxicity
Issue Date: Nov-2007
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
URI: http://scholarbank.nus.edu.sg/handle/10635/53133
ISSN: 13895575
DOI: 10.2174/138955707782331696
Appears in Collections:Staff Publications

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