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Medicinal Chemistry Research

, Volume 28, Issue 12, pp 2270–2278 | Cite as

Identification of prodigious and under-privileged structural features for RG7834 analogs as Hepatitis B virus expression inhibitor

  • Vijay H. MasandEmail author
  • Nahed N. E. El-Sayed
  • Vesna Rastija
  • Mithilesh M. Rathore
  • Maja Karnaš
Original Research
  • 47 Downloads

Abstract

In the present work, QSAR (quantitative structure−activity relationship) analysis has been executed for RG7834 analogs. RG7834 is a first-in-class selective and orally available dihydroquinolizinone (DHQ)-based small molecule Hepatitis B virus expression inhibitor. OECD’s guidelines have been followed for developing multiple QSAR models for Hepatitis B virus expression inhibitory activity of 73 RG7834 analogs. The present multiple QSAR models are not only easily interpretable but possess high external predictive ability, as well. These are effective in the recognition of many privileged and underprivileged molecular descriptors, which could be very valuable for the use of these models by the experts and nonexperts of QSAR in future optimizations. The models satisfy threshold values for many fitting, internal and external validation parameters, such as R2 = 0.83, Q2 = 0.80, CCCext = 0.88, etc., thereby demonstrating good external predictive ability of the models. The multiple QSAR and pharmacophoric models successfully identified a good number of important positively and negatively related structural features of RG7834 analogs that govern their Hepatitis B virus expression inhibitory activity. The results could be very beneficial to synthetic/medicinal chemists for future alterations of RG7834 analogs as better drug candidates.

Keywords

RG7834 analogs Hepatitis B virus Dihydroquinolizinone QSAR Pharmacophore modeling 

Abbreviations

HBV

Hepatitis B virus

MLR

Multiple linear regression

QSAR

Quantitative structure−activity analysis

WHO

World Health Organization

ADMET

Absorption, Distribution, Metabolism, Excretion and Toxicity

OLS

Ordinary least square

QSARINS

QSAR Insubria

OECD

Organisation for Economic Cooperation and Development

OFS

Objective feature selection

SFS

Subjective feature selection

Notes

Acknowledgements

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research (Group No. RG-1435-083). Authors are grateful to Dr. Paola Gramatica and QSARINS-Chem developing team for providing QSARINS-Chem 2.2.2.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

44_2019_2455_MOESM1_ESM.docx (28 kb)
Supplementary Information
44_2019_2455_MOESM2_ESM.docx (326 kb)
Supplementary Information

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Vijay H. Masand
    • 1
    Email author
  • Nahed N. E. El-Sayed
    • 2
    • 3
  • Vesna Rastija
    • 4
  • Mithilesh M. Rathore
    • 1
  • Maja Karnaš
    • 4
  1. 1.Department of ChemistryVidya Bharati MahavidyalayaAmravatiIndia
  2. 2.Department of Chemistry, College of Science, “Girls Section”King Saud UniversityRiyadhSaudi Arabia
  3. 3.National Organization for Drug Control and ResearchGizaEgypt
  4. 4.Department of Chemistry, Faculty of Agrobiotechnical SciencesJosip Juraj Strossmayer University of OsijekOsijekCroatia

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