Development of classification-and regression-based QSAR models and in silico screening of skin sensitisation potential of diverse organic chemicals

Ashis Nandy, Supratik Kar, Kunal Roy

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

With the advancement of technology and industrial revolution, skin sensitisation has emerged as a major environmental and occupational health hazard. A chemical may induce B-cell and T-cell infiltration from lymph nodes resulting in irritation of skin and carcinoma on prolonged use. To minimise the animal study and also to reduce time and expenditure, development of in silico predictive models has achieved a considerable attention over the last few decades. In this study, we have developed classification-and regression-based QSAR models for skin sensitisation potential of 67 diverse organic chemicals. The developed models strongly suggest the importance of the number of H atoms in a molecule inferring that the unsaturated compounds are more skin-sensitising agent than the saturated compounds. Other two descriptors, and nCb-, signify the importance of the lipophilic character of the molecules. Here, we have also screened the DrugBank database (http://www.drugbank.ca) containing a large number of compounds using our developed models in an attempt to identify molecules with skin sensitisation potential.

Original languageEnglish
Pages (from-to)261-274
Number of pages14
JournalMolecular Simulation
Volume40
Issue number4
DOIs
StatePublished - 16 Mar 2014

Keywords

  • EC3
  • LDA
  • OECD
  • QSAR
  • skin sensitisation

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