SMILES-based optimal descriptors: QSAR analysis of fullerene-based HIV-1 PR inhibitors by means of balance of correlations

Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, Danutad Leszczynska, Jerzy Leszczynski

Research output: Contribution to journalArticle

Abstract

Quantitative structure-activity relationships (QSAR) for prediction of binding affinities (pECSO, i.e., minus decimal logarithm of the 50% effective concentration) of 20 tullerene derivatives inhibitors of the HIV-1 PR (human immunodeficiency virus type 1 protease) have been developed by application of the optimal descriptors approach calculated with SMILES (simplified molecular input line entry system). The applied models were constructed by the balance of correlations. Three various splits of the experimental data into subtracting set, calibration set, and test set were examined. Comparison of classic scheme (training-test system) and the balance of correlations (subtraining-calibration- test system) show that the balance of correlations gives more robust predictions than the classic scheme for the pEC50 of the fullerene derivatives.

Original languageEnglish
Pages (from-to)381-392
Number of pages12
JournalJournal of Computational Chemistry
Volume31
Issue number2
DOIs
Publication statusPublished - Jan 30 2010

Keywords

  • Correlation balance
  • Fullrene
  • HIV-1 PR
  • Optimal descriptor
  • QSAR
  • SMILES

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

Fingerprint Dive into the research topics of 'SMILES-based optimal descriptors: QSAR analysis of fullerene-based HIV-1 PR inhibitors by means of balance of correlations'. Together they form a unique fingerprint.

  • Cite this