Molecular Diversity

, Volume 14, Issue 2, pp 225–235 | Cite as

A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs

  • Antreas Afantitis
  • Georgia Melagraki
  • Haralambos Sarimveis
  • Panayiotis A. Koutentis
  • Olga Igglessi-Markopoulou
  • George Kollias
Full-Length Paper


A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as “active” or “non-active” CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the “active” analogs a validated MLR QSAR model estimates accurately their I-IP10 IC50 inhibition values. The accuracy of the QSAR model (R 2 = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure \({(R^{2}_{\rm LOO} =0.67)}\) and validation through an external test set \({(R^{2}_{\rm pred} =0.78)}\). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor (1 κ), demonstrate discriminatory and pharmacophore abilities.


QSAR CXCR3 Molecular modelling In silico predictions 


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Antreas Afantitis
    • 1
    • 2
  • Georgia Melagraki
    • 3
  • Haralambos Sarimveis
    • 3
  • Panayiotis A. Koutentis
    • 4
  • Olga Igglessi-Markopoulou
    • 3
  • George Kollias
    • 1
  1. 1.Biomedical Sciences Research Center “Alexander Fleming”AthensGreece
  2. 2.Department of ChemInformaticsNovaMechanics LtdNicosiaCyprus
  3. 3.School of Chemical EngineeringNational Technical University of AthensAthensGreece
  4. 4.Department of ChemistryUniversity of CyprusNicosiaCyprus

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