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

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

Volume: 7 Issue: 11

Author(s): C. W. Yap, H. Li, Z. L. Ji and Y. Z. Chen

Affiliation:

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

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.

Cite this article as:

Yap W. C., Li H., Ji L. Z. and Chen Z. Y., Regression Methods for Developing QSAR and QSPR Models to Predict Compounds of Specific Pharmacodynamic, Pharmacokinetic and Toxicological Properties, Mini-Reviews in Medicinal Chemistry 2007; 7 (11) . https://dx.doi.org/10.2174/138955707782331696