SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL

Aleksandar M. Veselinović, Jovana B. Milosavljević, Andrey A. Toropov, Goran M. Nikolić

Research output: Contribution to journalArticlepeer-review

Abstract

A predictive quantitative structure - activity relationships model of arylpiperazines as high-affinity 5- HT1A receptor ligands was developed using CORAL software (http://www.insilico.eu/CORAL). Simplified molecular input-line entry system (SMILES) was used as representation of the molecular structure of the arylpiperazines. The balance of correlations was used in the Monte Carlo optimization aimed to build up optimal descriptors for one-variable models. The robustness of this model has been tested in four random splits into the sub-training, calibration, and test set. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r 2) for the test sets of the four random splits are 0.9459, 0.9249, 0.9473 and 0.9362.

Original languageEnglish
Pages (from-to)532-541
Number of pages10
JournalEuropean Journal of Pharmaceutical Sciences
Volume48
Issue number3
DOIs
Publication statusPublished - Feb 14 2013

Keywords

  • 5-HT receptor
  • Arylpiperazines
  • CORAL software
  • QSAR
  • SMILES

ASJC Scopus subject areas

  • Pharmaceutical Science
  • Medicine(all)

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