Skip to main content
SpringerLink
Log in

QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach

Medicinal Chemistry Research volume 24, pages 3037–3046 (2015)Cite this article

Abstract

The quantitative structure–activity relationship (QSAR) of the novel 4-aminopyrimidine-5-carbaldehyde oxime derivatives as effective and selective inhibitors of potent VEGFR-2 was studied. A suitable set of the molecular descriptors was calculated, and the most impressive descriptors were subsequently selected using genetic algorithm (GA) variable selection approach. To construct robust models, the GA was combined with multiple linear regression and support vector machine, respectively, as GA-MLR and GA-SVM. The predictive quality of the QSAR model was examined for an external set of six compounds, randomly chosen out of 32 compounds in the original data set. The accuracy of the proposed models was further confirmed using cross-validation, validation through an external test set and Y-randomization approaches. Based on the selected descriptors, we have identified some key features in the 4-aminopyrimidine-5-carbaldehyde oxime derivatives that are responsible for potent VEGFR-2 inhibitory activity. The analyses may be used to design more potent 4-aminopyrimidine-5-carbaldehyde oxime derivatives and predict their activities prior to synthesis.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  • Adimi M, Salimi M, Nekoei M, Pourbasheer E, Beheshti A (2012) A quantitative structure-activity relationship study on histamine receptor antagonists using the genetic algorithm-multi-parameter linear regression method. J Serb Chem Soc 77(5):639–650. doi:10.2298/jsc110804205a

    Article  CAS  Google Scholar 

  • Agrawal VK, Khadikar PV (2001) QSAR prediction of toxicity of nitrobenzenes. Bioorg Med Chem 9(11):3035–3040. doi:10.1016/s0968-0896(01)00211-5

    Article  CAS  PubMed  Google Scholar 

  • Ahmad S, Gromiha MM (2003) Design and training of a neural network for predicting the solvent accessibility of proteins. J Comput Chem 24(11):1313–1320. doi:10.1002/jcc.10298

    Article  CAS  PubMed  Google Scholar 

  • Aires-de-Sousa J, Hemmer MC, Gasteiger J (2002) Prediction of 1H-NMR chemical shifts using neural networks. Anal Chem 74(1):80–90. doi:10.1021/ac010737m

    Article  CAS  PubMed  Google Scholar 

  • Alsberg BK, Marchand-Geneste N, King RD (2000) A new 3D molecular structure representation using quantum topology with application to structure-property relationships. Chemom Intell Lab Syst 54:75–91

    Article  CAS  Google Scholar 

  • Baumann K (2005) Chance correlation in variable subset regression: influence of the objective function, the selection mechanism, and ensemble averaging. QSAR Comb Sci 24(9):1033–1046. doi:10.1002/qsar.200530134

    Article  CAS  Google Scholar 

  • Beheshti A, Pourbasheer E, Nekoei M, Banaei A (2012) Quantitative structure-activity relationship study of amino acid derivatives as histone deacetylase inhibitors using the genetic algorithm-multiple linear regression. Anal Chem Lett 2:33–43

    Article  CAS  Google Scholar 

  • Burbidge R, Trotter M, Buxton B, Holden S (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26(1):5–14. doi:10.1016/s0097-8485(01)00094-8

    Article  CAS  PubMed  Google Scholar 

  • Byvatov E, Fechner U, Sadowski J, Schneider G (2003) Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 43(6):1882–1889. doi:10.1021/ci0341161

    Article  CAS  PubMed  Google Scholar 

  • Caballero J, Fernandez M (2006) Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks. J Mol Model 12(2):168–181. doi:10.1007/s00894-005-0014-x

    Article  CAS  PubMed  Google Scholar 

  • Carmeliet P, Jain RK (2000) Angiogenesis in cancer and other diseases. Nature 407(6801):249–257

    Article  CAS  PubMed  Google Scholar 

  • Chen HF (2008) Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression. Anal Chim Acta 609(1):24–36. doi:10.1016/j.aca.2008.01.003

    Article  CAS  PubMed  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Google Scholar 

  • Depczynski U, Frost VJ, Molt K (2000) Genetic algorithms applied to the selection of factors in principal component regression. Anal Chim Acta 420:217–227

    Article  CAS  Google Scholar 

  • Dolatabadi M, Nekoei M, Banaei A (2010) Prediction of antibacterial activity of pleuromutilin derivatives by genetic algorithm-multiple linear regression (GA-MLR). Monatsh Chem 141(5):577–588. doi:10.1007/s00706-010-0299-z

    Article  CAS  Google Scholar 

  • Eriksson L, Gottfries J, Johansson E, Wold S (2004) Time-resolved QSAR: an approach to PLS modelling of three-way biological data. Chemom Intell Lab Syst 73(1):73–84. doi:10.1016/j.chemolab.2004.04.004

    Article  CAS  Google Scholar 

  • Fatemi MH, Gharaghani S (2007) A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine. Bioorg Med Chem 15(24):7746–7754. doi:10.1016/j.bmc.2007.08.057

    Article  CAS  PubMed  Google Scholar 

  • Fernandez M, Caballero J (2006) Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor. J Mol Graph Model 25(4):410–422. doi:10.1016/j.jmgm.2006.02.005

    Article  CAS  PubMed  Google Scholar 

  • Fernandez M, Caballero J (2007) QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-alpha-phenylsulfonylacetamide derivatives. Bioorg Med Chem 15(18):6298–6310. doi:10.1016/j.bmc.2007.06.014

    Article  CAS  PubMed  Google Scholar 

  • Ferrara N (1999) Molecular and biological properties of vascular endothelial growth factor. J Mol Med 77(7):527–543

    Article  CAS  PubMed  Google Scholar 

  • Ferrara N, Gerber HP, LeCouter J (2003) The biology of VEGF and its receptors. Nat Med 9(6):669–676. doi:10.1038/nm0603-669

    Article  CAS  PubMed  Google Scholar 

  • Folkman J (1995) Angiogenesis in cancer, vascular, rheumatoid and other disease. Nat Med 1(1):27–31

    Article  CAS  PubMed  Google Scholar 

  • Gunn SR (1997) Support vector machines for classification and regression. University of Southampton, Southampton

    Google Scholar 

  • Guo SC, Colbert LS, Fuller M, Zhang YY, Gonzalez-Perez RR (2010) Vascular endothelial growth factor receptor-2 in breast cancer. BBA-Rev Cancer 1806(1):108–121. doi:10.1016/j.bbcan.2010.04.004

    CAS  Google Scholar 

  • Hemmateenejad B, Safarpour MA, Miri R, Nesari N (2005) Toward an optimal procedure for PC-ANN model building: prediction of the carcinogenic activity of a large set of drugs. J Chem Inf Model 45(1):190–199. doi:10.1021/ci049766z

    Article  CAS  PubMed  Google Scholar 

  • Holmes K, Roberts OL, Thomas AM, Cross MJ (2007) Vascular endothelial growth factor receptor-2: structure, function, intracellular signalling and therapeutic inhibition. Cell Signal 19(10):2003–2012. doi:10.1016/j.cellsig.2007.05.013

    Article  CAS  PubMed  Google Scholar 

  • Huang SL, Li RH, LaMontagne KR, Greenberger LM, Connolly PJ (2011) 4-Aminopyrimidine-5-carbaldehyde oximes as potent VEGFR-2 inhibitors. Part II. Bioorg Med Chem Lett 21(6):1815–1818. doi:10.1016/j.bmcl.2011.01.053

    Article  CAS  PubMed  Google Scholar 

  • Hunger J, Huttner G (1999) Optimization and analysis of force field parameters by combination of genetic algorithms and neural networks. J Comput Chem 20:455–471

    Article  CAS  Google Scholar 

  • Jouanrimbaud D, Massart DL, Leardi R, deNoord OE (1995) Genetic algorithms as a tool for wavelength selection in multivariate calibration. Anal Chem 67:4295–4301

    Article  CAS  Google Scholar 

  • Kar S, Roy K (2010) QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. J Hazard Mater 177(1–3):344–351. doi:10.1016/j.jhazmat.2009.12.038

    Article  CAS  PubMed  Google Scholar 

  • Khadikar PV, Phadnis A, Shrivastava A (2002) QSAR study on toxicity to aqueous organisms using the PI index. Bioorg Med Chem 10(4):1181–1188. doi:10.1016/s0968-0896(01)00375-3

    Article  CAS  PubMed  Google Scholar 

  • Khajeh A, Modarress H (2012) Quantitative structure-property relationship prediction of liquid heat capacity at 298.15 K for organic compounds. Ind Eng Chem Res 51(17):6251–6255. doi:10.1021/ie202153e

    Article  CAS  Google Scholar 

  • Liu HX, Zhang RS, Luan F, Yao XJ, Liu MC, Hu ZD, Fan BT (2003a) Diagnosing breast cancer based on support vector machines. J Chem Inf Comput Sci 43(3):900–907. doi:10.1021/ci0256438

    Article  CAS  PubMed  Google Scholar 

  • Liu HX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT (2003b) QSAR study of ethyl 2-(3-methyl-2,5-dioxo(3-pyrrolinyl))amino-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-I and NF-kappa B mediated gene expression based on support vector machines. J Chem Inf Comput Sci 43(4):1288–1296. doi:10.1021/ci0340355

    Article  CAS  PubMed  Google Scholar 

  • Liu HX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT (2004) Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs. J Chem Inf Comput Sci 44(1):161–167. doi:10.1021/ci034173u

    Article  CAS  PubMed  Google Scholar 

  • Liu J, Zhou L, Zuo ZL (2008) Antibacterial activities of carbapenem derivatives and quantitative structure-activity relationship for drug design. QSAR Comb Sci 27(10):1216–1226. doi:10.1002/qsar.200710104

    Article  CAS  Google Scholar 

  • Lohela M, Bry M, Tammela T, Alitalo K (2009) VEGFs and receptors involved in angiogenesis versus lymphangiogenesis. Curr Opin Cell Biol 21(2):154–165. doi:10.1016/j.ceb.2008.12.012

    Article  CAS  PubMed  Google Scholar 

  • Mohammadhosseini M, Deeb O, Alavi- Gharabagh A, Nekoei M (2012) Exploring novel QSRRs for simulation of gas chromatographic retention indices of diverse sets of terpenoids in Pistacia lentiscus L. essential oil using stepwise and genetic algorithm multiple linear regressions. Anal Chem Lett 2:80–102

    Article  CAS  Google Scholar 

  • Nekoei M, Salimi M, Dolatabadi M, Mohammadhosseini M (2011a) Prediction of antileukemia activity of berbamine derivatives by genetic algorithm-multiple linear regression. Monatsh Chem 142(9):943–948. doi:10.1007/s00706-011-0510-x

    Article  CAS  Google Scholar 

  • Nekoei M, Salimi M, Dolatabadi M, Mohammadhosseini M (2011b) A quantitative structure-activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors agonists. J Serb Chem Soc 76(8):1117–1127. doi:10.2298/jsc101122102s

    Article  CAS  Google Scholar 

  • Niani C, Wencong L, Jie Y, Gozheng L (2004) Support vector machine in chemistry. World Scientific Publishing Co. Pet. Ltd., Shanghai

    Google Scholar 

  • Pandey VK, Tusi Z, Tandon M, Joshi MN, Bajpai SK (2003) Synthesis of thiadiazolo-S-triazines for their antiviral activity based on QSAR studies. Indian J Chem Sect B 42(10):2583–2588

    Google Scholar 

  • Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2010) Quantitative structure-activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method. J Enzyme Inhib Med Chem 25(6):844–853. doi:10.3109/14756361003757893

    Article  CAS  PubMed  Google Scholar 

  • Riahi S, Ganjali MR, Pourbasheer E, Norouzi P (2008) QSRR study of GC retention indices of essential-oil compounds by multiple linear regression with a genetic algorithm. Chromatographia 67(11–12):917–922. doi:10.1365/s10337-008-0608-4

    Article  CAS  Google Scholar 

  • Riahi S, Pourbasheer E, Ganjali MR, Norouzi P (2009) Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. J Hazard Mater 166(2–3):853–859. doi:10.1016/j.jhazmat.2008.11.097

    Article  CAS  PubMed  Google Scholar 

  • Stitson MO, Weston JAE, Gammerman A, Vork V, Vapnik VN (1996) Theory of support vector machines, technical report CSD-TR-96-17. Department of Computer Science, Royal Holloway College, University of London

  • Toropov AA, Benfenati E (2006a) QSAR models for Daphnia toxicity of pesticides based on combinations of topological parameters of molecular structures. Bioorg Med Chem 14(8):2779–2788. doi:10.1016/j.bmc.2005.11.060

    Article  CAS  PubMed  Google Scholar 

  • Toropov AA, Benfenati E (2006b) QSAR models of quail dietary toxicity based on the graph of atomic orbitals. Bioorg Med Chem Lett 16(7):1941–1943. doi:10.1016/j.bmcl.2005.12.085

    Article  CAS  PubMed  Google Scholar 

  • Tropsha A, Zhang WF (2001) Identification of the descriptor pharmacophores using variable selection QSAR: applications to database mining. Curr Pharm Des 7(7):599–612. doi:10.2174/1381612013397834

    Article  CAS  PubMed  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin

    Book  Google Scholar 

  • Vapnik NV (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Vatani A, Mehrpooya M, Gharagheizi F (2007) Prediction of standard enthalpy of formation by a QSPR model. Int J Mol Sci 8(5):407–432. doi:10.3390/i8050407

    Article  CAS  PubMed Central  Google Scholar 

  • Waller CL, Bradley MP (1999) Development and validation of a novel variable selection technique with application to multidimensional quantitative structure–activity relationship studies. J Chem Inf Comput Sci 39:345–355

    Article  CAS  Google Scholar 

  • Xu J, Zhang H, Wang L, Liang GJ, Wang LX, Shen XL, Xu WL (2010) QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors. Spectrochim Acta Mol Biomol Spectros 76(2):239–247. doi:10.1016/j.saa.2010.03.027

    Article  Google Scholar 

  • Zhao WG, Wang HG, Li ZM, Yang Z (2006) Synthesis and antiviral activity against tobacco mosaic virus and 3D-QSAR of alpha-substituted-1,2,3-thiadiazoleacetamides. Bioorg Med Chem Lett 16(23):6107–6111. doi:10.1016/j.bmcl.2006.05.043

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors express their special thanks to the office for research affair of Islamic Azad University of Shahrood for financial supports of the research proposal and the time dedicated for performance the calculations of the study.

Author information

Authors and Affiliations

  1. Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran

    Mehdi Nekoei & Majid Mohammadhosseini

  2. Department of Chemistry, Payame Noor University, P. O. Box 19395-3697, Tehran, Iran

    Eslam Pourbasheer

Authors
  1. Mehdi Nekoei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Majid Mohammadhosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eslam Pourbasheer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Nekoei.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nekoei, M., Mohammadhosseini, M. & Pourbasheer, E. QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach. Med Chem Res 24, 3037–3046 (2015). https://doi.org/10.1007/s00044-015-1354-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00044-015-1354-4

Keywords

search

Navigation