Medicinal Chemistry Research

, Volume 26, Issue 12, pp 3203–3208 | Cite as

QSAR of antimycobacterial activity of benzoxazoles by optimal SMILES-based descriptors

  • Karel Nesměrák
  • Andrey A. Toropov
  • Alla P. Toropova
  • Tugba Ertan-Bolelli
  • Ilkay Yildiz
Original Research
  • 117 Downloads

Abstract

The CORAL software based on the optimal descriptors calculated with simplified molecular input-line entry system was used to build up quantitatively structure-activity relationship for the minimal inhibition concentrations against (i) Mycobacterium tuberculosis H37RV ATCC 27294 and (ii) M. tuberculosis drug resistant clinical isolate. The predictive potential of these models has been checked up with three random splits into the training, calibration, and validation sets. Although all models are quantitative, it has been found that splitting have apparent influence on the statistical quality of these models. Thus, the thesis “QSAR is a random event” is confirmed in this work.

Keywords

QSAR Antimycobacterial activity Benzoxazoles CORAL software SMILES 

Notes

Acknowledgements

A.A.T. and A.P.T thank the project LIFE-COMBASE contract (LIFE15 ENV/ES/000416) for financial support. I.Y. and T.E.-B. thank the Research Fund of Ankara University (Grant No: 12B3336002) for the financial support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. ACD/ChemSketch (2016) Advanced Chemistry Development, Toronto, Canada. http://www.acdlabs.com/products/draw_nom/draw/chemsketch/. Accessed 20 June 2016
  2. Achary PGR (2014a) Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2. SAR QSAR Environ Res 25:73–90CrossRefPubMedGoogle Scholar
  3. Achary PGR (2014b) QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software. SAR QSAR Environ Res 25:507–526CrossRefPubMedGoogle Scholar
  4. Calligaro GL, Moodley L, Symons G, Dheda K (2014) The medical and surgical treatment of drug-resistant tuberculosis. J Thorac Dis 6:186–195PubMedPubMedCentralGoogle Scholar
  5. Cassano A, Robinson RLM, Palczewska A, Puzyn T, Gajewicz A, Tran L, Manganelli S, Cronin MTD (2016) Comparing the CORAL and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials. ATLA Altern Lab Anim 44:533–556PubMedGoogle Scholar
  6. Connolly MY (2010) Quantitative drug design, a critical introduction, 2nd edn. CRC Press, Boca RatonGoogle Scholar
  7. CORAL (2016) http://www.insilico.eu/CORAL. Accessed 20 June 2016
  8. Demmer ChS, Bunch L (2015) Benzoxazoles and oxazolopyridines in medicinal chemistry studies. Eur J Med Chem 97:778–785CrossRefPubMedGoogle Scholar
  9. Ertan-Bolelli T, Yildiz I, Ozgen-Ozgacar S (2016) Synthesis, molecular docking and antimicrobial evaluation of novel benzoxazole derivatives. Med Chem Res 25:553–567CrossRefGoogle Scholar
  10. Fatemi MH, Malekzadeh H (2015) CORAL: Predictions of retention indices of volatiles in cooking rice using representation of the molecular structure obtained by combination of SMILES and graph approaches. J Iran Chem Soc 12:405–412CrossRefGoogle Scholar
  11. Garro Martinez JC, Duchowicz PR, Estrada MR, Zamarbide GN, Castro EA (2011) QSAR study and molecular design of open-chain enaminones as anticonvulsant agents. Int J Mol Sci 12:9354–9368CrossRefPubMedPubMedCentralGoogle Scholar
  12. Ghaedi A (2015) Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors. J Mol Liq 208:269–279CrossRefGoogle Scholar
  13. Heidari A, Fatemi MH (2017) A theoretical approach to model and predict the adsorption coefficients of some small aromatic molecules on carbon nanotube. J Chinese Chem Soc 64:289–295CrossRefGoogle Scholar
  14. Ibezim E, Duchowicz PR, Ortiz EV, Castro EA (2012) QSAR on aryl-piperazine derivatives with activity on malaria. Chemom Intell Lab Syst 110:81–88CrossRefGoogle Scholar
  15. Islam MA, Pillay TS (2016) Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemometr Intell Lab Syst 153:67–74CrossRefGoogle Scholar
  16. Kumar A, Chauhan S (2017a) QSAR differential model for prediction of SIRT1 modulation using monte carlo method. Drug Res 67:156–162Google Scholar
  17. Kumar A, Chauhan S (2017b) Use of the monte carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors. Arch Pharm 350, article no. e1600268Google Scholar
  18. Li Q, Ding X, Si H, Gao H (2014) QSAR model based on SMILES of inhibitory rate of 2, 3-diarylpropenoic acids on AKR1C3. Chemometr Intell Lab Syst 139:132–138CrossRefGoogle Scholar
  19. Mullen LMA, Duchowicz PR, Castro EA (2011) QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents. Chemom Intell Lab Syst 107:269–275CrossRefGoogle Scholar
  20. Poce G, Cocozza M, Consalvi S, Biava M (2014) SAR analysis of new anti-TB drugs currently in pre-clinical and clinical development. Eur J Med Chem 2014; 86:335–351CrossRefPubMedGoogle Scholar
  21. Rana DN, Chhabria MT, Shah NK, Brahmkshatriya PS (2014) Pharmacophore combination as a useful strategy to discover new antitubercular agents. Med Chem Res 23:370–381CrossRefGoogle Scholar
  22. Siddiqi N, Siddiqi MI (2014) Recent advances in QSAR-based identification and design of anti-tubercular agents. Curr Pharm Des 20:4418–4426CrossRefPubMedGoogle Scholar
  23. Sokolović D, Aleksić D, Milenković V, Karaleić S, Mitić D, Kocić J, Mekić B, Veselinović JB, Veselinović AM (2016a) QSAR modeling of bis-quinolinium and bis-isoquinolinium compounds as acetylcholine esterase inhibitors based on the Monte Carlo method—the implication for Myasthenia gravis treatment. Med Chem Res 25:2989–2998CrossRefGoogle Scholar
  24. Sokolović D, Ranković J, Stanković V, Stefanović R, Karaleić S, Mekić B, Milenković V, Kocić J, Veselinović AM (2017) QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med Chem Res 26:796–804CrossRefGoogle Scholar
  25. Sokolović D, Stanković V, Toskić D, Lilić L, Ranković G, Ranković J, Nedin-Ranković G, Veselinović AM (2016b) Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct Chem 27:1511–1519CrossRefGoogle Scholar
  26. Toropov AA, Toropova AP, Benfenati E, Fanelli R (2016) QSAR as a random event: Selecting of the molecular structure for potential anti-tuberculosis agents. Anti-Infect Agents 14:3–10CrossRefGoogle Scholar
  27. Toropov AA, Toropova AP, Benfenati E, Gini G, Fanelli R (2013a) The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method. Struct Chem 24:1369–1381CrossRefGoogle Scholar
  28. Toropov AA, Toropova AP, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2010) QSAR analysis of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines exhibiting anticancer activity by optimal SMILES-based descriptors. J Math Chem 47:647–666CrossRefGoogle Scholar
  29. Toropov AA, Toropova AP, Benfenati E, Nicolotti O, Carotti A, Nesmerak K, Veselinović AM, Veselinović JB, Duchowicz PR, Bacelo D, Castro EA, Rasulev BF, Leszczynska D, Leszczynski J (2015) QSPR/QSAR analyses by means of the CORAL software: results, challenges, perspectives. In: Roy K (ed) Quantitative structure-activity relationships in drug design, predictive toxicology and risk assessment. IGI Global, Hershey, pp 560–585CrossRefGoogle Scholar
  30. Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynksi J (2013b) QSAR as a random event: Models for nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92:31–37CrossRefPubMedGoogle Scholar
  31. Toropova AP, Toropov AA, Benfenati E, Gini G (2011c) Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances. An unexpected good prediction based on a model that seems untrustworthy. Chemom Intell Lab Syst 105:215–219CrossRefGoogle Scholar
  32. Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011a) CORAL: Quantitative structure - activity relationship models for estimating toxicity of organic compounds in rats. J Comput Chem 32:2727–2733CrossRefPubMedGoogle Scholar
  33. Toropova AP, Toropov AA, Diaza RG, Benfenati E, Gini G (2011b) Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity. An unexpected good prediction based on a model that seems untrustworthy. Cent Eur J Chem 9:165–174Google Scholar
  34. Toropova AP, Toropov AA, Martyanov SE, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2012) CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna. Chemom Intell Lab Syst 110:177–181CrossRefGoogle Scholar
  35. Veselinović AM, Veselinović JB, Živković JV, Nikolić GM (2015) Application of smiles notation based optimal descriptors in drug discovery and design. Cur Top Med Chem 15:1768–1779CrossRefGoogle Scholar
  36. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (2014) QSAR study of H1N1 neuraminidase inhibitors from influenza a virus. Lett Drug Des Discov 11:420–427CrossRefGoogle Scholar
  37. Živković JV, Trutić NV, Veselinović JB, Nikolić GM, Veselinović AM (2015) Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. Comput Biol Med 64:276–282CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Analytical ChemistryCharles University in Prague, Faculty of SciencePrague 2Czech Republic
  2. 2.IRCCS-Istituto di Ricerche Farmacologiche Mario NegriMilanoItaly
  3. 3.Department of Pharmaceutical ChemistryFaculty of Pharmacy, Ankara UniversityAnkaraTurkey

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