Predictive toxicity modelling of benzodiazepine drugs using multiple in silico approaches: Descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping

Supratik Kar, Kunal Roy

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Benzodiazepines have been widely used therapeutically for their ability to act as tranquilizers, sedative-hypnotics, antiepileptics and frequently prescribed to women during pregnancy for managing preeclampsia or eclampsia. The present report deals with quantitative structure-toxicity relationship (QSTR) modelling of a series of benzodiazepines, in the context of the 3R concept, to provide an insight into the main structural fragments that impart toxicity to these molecules. Three different in silico techniques, namely descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping, were employed to obtain statistically significant models. Multiple in silico models made it possible to reach a unified conclusion regarding the structural fragments and features responsible for the toxicity and provide consensus predictions which can be effectively utilised to design and predict less toxic new benzodiazepines.

Original languageEnglish
Pages (from-to)345-355
Number of pages11
JournalMolecular Simulation
Volume41
Issue number4
DOIs
StatePublished - 4 Mar 2015

Keywords

  • benzodiazepines
  • in silico
  • QSAR
  • QSTR
  • toxicophore

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