Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods

C. W. Yap, Y. Xue, H. Li, Z. R. Li, C. Y. Ung, L. Y. Han, C. J. Zheng, Z. W. Cao, Y. Z. Chen

Research output: Contribution to journalReview articlepeer-review

26 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)449-459
Number of pages11
JournalMini-Reviews in Medicinal Chemistry
Volume6
Issue number4
DOIs
StatePublished - Apr 1 2006

Keywords

  • Molecular descriptors
  • Pharmacodynamic
  • Pharmacokinetic
  • QSAR
  • QSPR
  • Statistical learning methods
  • Structural diversity
  • Toxicology

ASJC Scopus subject areas

  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Cancer Research

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