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Molecular Diversity

, Volume 13, Issue 3, pp 301–311 | Cite as

Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors

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

Abstract

A linear Quantitative Structure–Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl-thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds.

Keywords

HDAC Hydroxamic acids QSAR In silico screening Histone deacetylases 

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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Georgia Melagraki
    • 1
    • 2
    • 3
  • Antreas Afantitis
    • 1
    • 2
    • 3
    • 5
  • Haralambos Sarimveis
    • 3
  • Panayiotis A. Koutentis
    • 4
  • George Kollias
    • 5
  • Olga Igglessi-Markopoulou
    • 3
  1. 1.Department of ChemoInformaticsNovaMechanics LtdLarnacaCyprus
  2. 2.Cyano Research Corporation LtdNicosiaCyprus
  3. 3.School of Chemical EngineeringNational Technical University of AthensAthensGreece
  4. 4.Department of ChemistryUniversity of CyprusNicosiaCyprus
  5. 5.Biomedical Sciences Research Center “Alexander Fleming”AthensGreece

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