The application of in silico drug-likeness predictions in pharmaceutical research

Sheng Tian, Junmei Wang, Youyong Li, Dan Li, Lei Xu, Tingjun Hou

Research output: Contribution to journalArticle

121 Scopus citations

Abstract

The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.

Original languageEnglish (US)
Pages (from-to)2-10
Number of pages9
JournalAdvanced Drug Delivery Reviews
Volume86
DOIs
StatePublished - Jun 23 2015

Keywords

  • ADMET
  • Computer-aided drug design
  • Drug-likeness
  • Machine learning
  • Traditional Chinese medicines
  • Virtual screening

ASJC Scopus subject areas

  • Pharmaceutical Science

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