Generic placeholder image

Mini-Reviews in Medicinal Chemistry


ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

Prediction of Compounds with Specific Pharmacodynamic, Pharmacokinetic or Toxicological Property by Statistical Learning Methods

Author(s): C. W. Yap, Y. Xue, H. Li, Z. R. Li, C. Y. Ung, L. Y. Han, C. J. Zheng, Z. W. Cao and Y. Z. Chen

Volume 6, Issue 4, 2006

Page: [449 - 459] Pages: 11

DOI: 10.2174/138955706776361501

Price: $65


Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.

Keywords: Statistical learning methods, pharmacodynamic, pharmacokinetic, toxicology, QSAR, QSPR, molecular descriptors, structural diversity

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy