Molecular Diversity

, Volume 19, Issue 4, pp 955–964 | Cite as

Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors

  • Apilak Worachartcheewan
  • Virapong Prachayasittikul
  • Alla P. Toropova
  • Andrey A. Toropov
  • Chanin Nantasenamat
Full-Length Paper


Hepatitis C virus (HCV) is composed of structural and non-structural proteins involved in viral transcription and propagation. In particular, NS5B is an RNA-dependent RNA polymerase for viral transcription and genome replication and is a target for designing anti-viral agents. In this study, classification and quantitative structure-activity relationship (QSAR) models of HCV NS5B inhibitors were constructed using the Correlation and Logic software. Molecular descriptors for a set of 970 HCV NS5B inhibitors were encoded using the simplified molecular input line entry system notation, and predictive models were built via the Monte Carlo method. The QSAR models provided acceptable correlation coefficients of \(R^{2}\) and \(Q^{2}\) in the ranges of 0.6038–0.7344 and 0.6171–0.7294, respectively, while the classification models displayed sensitivity, specificity, and accuracy in ranges of 88.24–98.84, 83.87–93.94, and 86.50–94.41 %, respectively. Furthermore, molecular fragments as substructures involved in increased and decreased inhibitory activities were explored. The results provide information on QSAR and classification models for high-throughput screening and mechanistic insights into the inhibitory activity of HCV NS5B polymerase.


Hepatitis C virus HCV NS5B polymerase inhibitors Monte Carlo method Structure-activity relationship Data mining 



This research project is supported by the annual budget grant (B.E. 2557-2559) and Talent Management Program (AW) from Mahidol University. Partial support is also acknowledged from the Office of the Higher Education Commission and Mahidol University under the National Research Universities Initiative. APT and AAT are grateful for financial support of the EU project PROSIL funded under the LIFE program (project LIFE12ENV/IT/000154).

Compliance with Ethical Standards

Conflicts of interest

The authors declare that there are no known conflicts of interest.

Supplementary material

11030_2015_9614_MOESM1_ESM.pdf (3.8 mb)
Supplementary material 1 (pdf 3933 KB)


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Apilak Worachartcheewan
    • 1
    • 2
  • Virapong Prachayasittikul
    • 3
  • Alla P. Toropova
    • 4
  • Andrey A. Toropov
    • 4
  • Chanin Nantasenamat
    • 1
    • 3
  1. 1.Center of Data Mining and Biomedical Informatics, Faculty of Medical TechnologyMahidol UniversityBangkokThailand
  2. 2.Department of Clinical Chemistry, Faculty of Medical Technology Mahidol UniversityBangkokThailand
  3. 3.Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol UniversityBangkokThailand
  4. 4.IRCCS-Istituto di Ricerche Farmacologiche Mario NegriMilanItaly

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