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Regression Methods for Developing QSAR and QSPR Models to Predict Compounds of Specific Pharmacodynamic, Pharmacokinetic and Toxicological Properties

[ Vol. 7 , Issue. 11 ]

Author(s):

C. W. Yap, H. Li, Z. L. Ji and Y. Z. Chen   Pages 1097 - 1107 ( 11 )

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 algorithms of these methods, evaluates their advantages and disadvantages, and discusses the application potential of the recently explored methods. Freely available online and commercial software for these regression methods and the areas of their applications are also presented.

Keywords:

ADME, ADMET, compound, drug, pharmacodynamics, pharmacokinetics, toxicity, QSAR, QSPR, statistical learning methods

Affiliation:

Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Block S4, 18 Science Drive 4, Singapore 117543.



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