Skip to main content
Log in

Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models

  • Full-Length Paper
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with \(\hbox {IC}_{50}\) values in the range of 12.9–185.0 nM, and against H3N2 NA with \(\hbox {IC}_{50}\) values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with \(\hbox {IC}_{50}\) values ranging 39.5–63.8 \(\upmu \)M against H1N1 NA and 44.5–114.1 \(\upmu \)M against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays.

This is a preview of subscription content, log in via an institution to check access.

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Fiore AE, Bridges CB, Cox NJ (2009) Seasonal influenza vaccines. Curr Top Microbiol Immunol 333:43–82. doi:10.1007/978-3-540-92165-3_3

  2. Bouvier NM, Palese P (2008) The biology of influenza viruses. Vaccine 26:D49–D53. doi:10.1016/j.vaccine.2008.07.039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lee SM, Yen HL (2012) Targeting the host or the virus: current and novel concepts for antiviral approaches against influenza virus infection. Antiviral Res 96:391–404. doi:10.1016/j.antiviral.2012.09.013

    Article  CAS  PubMed  Google Scholar 

  4. Elliott M (2001) Zanamivir: from drug design to the clinic. Philos Trans R Soc Lond B Biol Sci 356:1885–1893. doi:10.1098/rstb.2001.1021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kelly H, Cowling BJ (2015) Influenza: the rational use of oseltamivir. Lancet 385:1700–1702. doi:10.1016/S0140-6736(15)60074-5

    Article  PubMed  Google Scholar 

  6. Spanakis N, Pitiriga V, Gennimata V, Tsakris A (2014) A review of neuraminidase inhibitor susceptibility in influenza strains. Expert Rev Anti Infect Ther 12:1325–1336. doi:10.1586/14787210.2014.966083

    Article  CAS  PubMed  Google Scholar 

  7. Yoneda M, Okayama A, Kitahori Y (2014) Oseltamivir-resistant seasonal A(H1N1) and A(H1N1)pdm09 influenza viruses from the 2007/2008 to 2012/2013 season in Nara Prefecture, Japan. Jpn J Infect Dis 67:385–388. doi:10.7883/yoken.67.385

    Article  CAS  PubMed  Google Scholar 

  8. Kongkamnerd J, Milani A, Cattoli G, Terregino C, Capua I, Beneduce L, Gallotta A, Pengo P, Fassina G, Miertus S, De-Eknamkul W (2012) A screening assay for neuraminidase inhibitors using neuraminidases N1 and N3 from a baculovirus expression system. J Enzyme Inhib Med Chem 27:5–11. doi:10.3109/14756366.2011.568415

    Article  CAS  PubMed  Google Scholar 

  9. Guo CT, Takahashi T, Bukawa W, Takahashi N, Yagi H, Kato K, Hidari KI, Miyamoto D, Suzuki T, Suzuki Y (2006) Edible bird’s nest extract inhibits influenza virus infection. Antiviral Res 70:140–146. doi:10.1016/j.antiviral.2006.02.005

    Article  CAS  PubMed  Google Scholar 

  10. Yang F, Zhou WL, Liu AL, Qin HL, Lee SM, Wang YT, Du GH (2012) The protective effect of 3-deoxysappanchalcone on in vitro influenza virus-induced apoptosis and inflammation. Planta Med 78:968–973. doi:10.1055/s-0031-1298620

    Article  CAS  PubMed  Google Scholar 

  11. Zu M, Yang F, Zhou W, Liu A, Du G, Zheng L (2012) In vitro anti-influenza virus and anti-inflammatory activities of theaflavin derivatives. Antiviral Res 94:217–224. doi:10.1016/j.antiviral.2012.04.001

    Article  CAS  PubMed  Google Scholar 

  12. Lushington GH (2014) Editorial: mining for pharmacophores in phenotypic screens. Comb Chem High Throughput Screen 17:651. doi:10.2174/138620731708140922155612

    Article  CAS  PubMed  Google Scholar 

  13. Heikamp K, Bajorath J (2014) Support vector machines for drug discovery. Expert Opin Drug Discov 9:93–104. doi:10.1517/17460441.2014.866943

    Article  CAS  PubMed  Google Scholar 

  14. Gertrudes JC, Maltarollo VG, Silva RA, Oliveira PR, Honorio KM, da Silva AB (2012) Machine learning techniques and drug design. Curr Med Chem 19:4289–4297. doi:10.2174/092986712802884259

    Article  CAS  PubMed  Google Scholar 

  15. Bender A (2011) Bayesian methods in virtual screening and chemical biology. Methods Mol Biol 672:175–196. doi:10.1007/978-1-60761-839-3_7

  16. Zou J, Han Y, So SS (2008) Overview of artificial neural networks. Methods Mol Biol 458:15–23. doi:10.1007/978-1-60327-101-1

    PubMed  Google Scholar 

  17. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random Forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947–1958. doi:10.1021/ci034160g

    Article  CAS  PubMed  Google Scholar 

  18. Chekmarev D, Kholodovych V, Kortagere S, Welsh WJ, Ekins S (2009) Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors. Pharm Res 26:2216–2224. doi:10.1007/s11095-009-9937-8

    Article  CAS  PubMed  Google Scholar 

  19. Yan A, Hu X, Wang K, Sun J (2013) Discriminating of ATP competitive Src kinase inhibitors and decoys using self-organizing map and support vector machine. Mol Divers 17:75–83. doi:10.1007/s11030-012-9411-0

    Article  CAS  PubMed  Google Scholar 

  20. Cheng F, Yu Y, Shen J, Yang L, Li W, Liu G, Lee PW, Tang Y (2011) Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J Chem Inf Model 51:996–1011. doi:10.1021/ci200028n

    Article  CAS  PubMed  Google Scholar 

  21. Fang J, Yang R, Gao L, Zhou D, Yang S, Liu AL, Du GH (2013) Predictions of BuchE inhibitors using support vector machine and Naive Bayesian classification techniques in drug discovery. J Chem Inf Model 53:3009–3020. doi:10.1021/ci400331p

    Article  CAS  PubMed  Google Scholar 

  22. Fang J, Yang R, Gao L, Yang S, Pang X, Li C, He Y, Liu AL, Du GH (2015) Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery. Mol Divers 19:149–162. doi:10.1007/s11030-014-9561-3

    Article  CAS  PubMed  Google Scholar 

  23. Fang J, Li Y, Liu R, Pang X, Li C, Yang R, He Y, Lian W, Liu A, Du G (2015) Discovery of multi-target-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J Chem Inf Model 55:149–164. doi:10.1021/ci500574n

    Article  CAS  PubMed  Google Scholar 

  24. Cong Y, Li B, Yang X, Xue Y, Chen Y, Zeng Y (2013) Quantitative structure-activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression. Chemom Intell Lab Syst 127:35–42. doi:10.1016/j.chemolab.2013.05.012

    Article  CAS  Google Scholar 

  25. Wei XY, Meng QW (2013) Classification prediction of inhibitors of H1N1 neuraminidase by machine learning methods. Acta Phys Chin Sin 29:217–223. doi:10.3866/PKU.WHXB201211122

    Google Scholar 

  26. Wang Y, Ge H, Li Y, Xie Y, He Y, Xu M, Gu Q, Xu J (2015) Predicting dual-targeting anti-influenza agents using multi-models. Mol Divers 19:123–134. doi:10.1007/s11030-014-9552-4

    Article  PubMed  Google Scholar 

  27. Li C, Fang JS, Lian WW, Pang XC, Liu AL, Du GH (2015) In vitro antiviral effects and 3D QSAR study of resveratrol derivatives as potent inhibitors of influenza H1N1 neuraminidase. Chem Biol Drug Des 85:427–438. doi:10.1111/cbdd.12425

    Article  CAS  PubMed  Google Scholar 

  28. Brouillette WJ, Atigadda VR, Luo M, Air GM, Babu YS, Bantia S (1999) Design of benzoic acid inhibitors of influenza neuraminidase containing a cyclic substitution for the N-acetyl grouping. Bioorg Med Chem Lett 9:1901–1906. doi:10.1016/S0960-894X(99)00318-2

    Article  CAS  PubMed  Google Scholar 

  29. Chand P, Babu YS, Bantia S, Chu N, Cole LB, Kotian PL, Laver WG, Montgomery JA, Pathak VP, Petty SL, Shrout DP, Walsh DA, Walsh GM (1997) Design and synthesis of benzoic acid derivatives as influenza neuraminidase inhibitors using structure-based drug design. J Med Chem 40:4030–4052. doi:10.1021/jm970479e

    Article  CAS  PubMed  Google Scholar 

  30. Chand P, Babu YS, Bantia S, Rowland S, Dehghani A, Kotian PL, Hutchison TL, Ali S, Brouillette W, El-Kattan Y, Lin TH (2004) Syntheses and neuraminidase inhibitory activity of multisubstituted cyclopentane amide derivatives. J Med Chem 40:1919–1929. doi:10.1021/jm0303406

    Article  Google Scholar 

  31. Chand P, Kotian PL, Dehghani A, El-Kattan Y, Lin TH, Hutchison TL, Babu YS, Bantia S, Elliott AJ, Montgomery JA (2001) Systematic structure-based design and stereoselective synthesis of novel multisubstituted cyclopentane derivatives with potent antiinfluenza activity. J Med Chem 44:4379–4392. doi:10.1021/jm010277p

    Article  CAS  PubMed  Google Scholar 

  32. Chand P, Kotian PL, Morris PE, Bantia S, Walsh DA, Babu YS (2005) Synthesis and inhibitory activity of benzoic acid and pyridine derivatives on influenza neuraminidase. Bioorg Med Chem 13:2665–2678. doi:10.1016/j.bmc.2005.01.042

    Article  CAS  PubMed  Google Scholar 

  33. Kim CU, Lew W, Williams MA, Wu H, Zhang L, Chen X, Escarpe PA, Mendel DB, Laver WG, Stevens RC (1998) Structure-activity relationship studies of novel carbocyclic influenza neuraminidase inhibitors. J Med Chem 41:2451–2460. doi:10.1021/jm980162u

    Article  CAS  PubMed  Google Scholar 

  34. Lew W, Wu H, Chen X, Graves BJ, Escarpe PA, MacArthur HL, Mendel DB, Kim CU (2000) Carbocyclic influenza neuraminidase inhibitors possessing a C3-cyclic amine side chain: synthesis and inhibitory activity. Bioorg Med Chem Lett 10:1257–1260. doi:10.1016/S0960-894X(00)00214-6

    Article  CAS  PubMed  Google Scholar 

  35. Lew W, Wu H, Mendel DB, Escarpe PA, Chen X, Laver WG, Graves BJ, Kim CU (1998) A new series of C3-aza carbocyclic influenza neuraminidase inhibitors: synthesis and inhibitory activity. Bioorg Med Chem Lett 8:3321–3324. doi:10.1016/S0960-894X(98)00587-3

    Article  CAS  PubMed  Google Scholar 

  36. Smith PW, Sollis SL, Howes PD, Cherry PC, Starkey ID, Cobley KN, Weston H, Scicinski J, Merritt A, Whittington A, Wyatt P, Taylor N, Green D, Bethell R, Madar S, Fenton RJ, Morley PJ, Pateman T, Beresford A (1998) Dihydropyrancarboxamides related to zanamivir: a new series of inhibitors of influenza virus sialidases. 1. Discovery, synthesis, biological activity, and structure-activity relationships of 4-guanidino- and 4-amino-4H-pyran-6-carboxamides. J Med Chem 41:787–797. doi:10.1021/jm970374b

    Article  CAS  PubMed  Google Scholar 

  37. Zhang L, Williams MA, Mendel DB, Escarpe PA, Chen X, Wang KY, Graves BJ, Lawton G, Kim CU (1999) Synthesis and evaluation of 1,4,5,6-tetrahydropyridazine derivatives as influenza neuraminidase inhibitors. Bioorg Med Chem Lett 9:1751–1756. doi:10.1016/S0960-894X(99)00280-2

    Article  CAS  PubMed  Google Scholar 

  38. Chen CL, Lin TC, Wang SY, Shie JJ, Tsai KC, Cheng YS, Jan JT, Lin CJ, Fang JM, Wong CH (2014) Tamiphosphor monoesters as effective anti-influenza agents. Eur J Med Chem 81:106–118. doi:10.1016/j.ejmech.2014.04.082

    Article  CAS  PubMed  Google Scholar 

  39. Dao TT, Nguyen PH, Lee HS, Kim E, Park J, Lim SI, Oh WK (2011) Chalcones as novel influenza A (H1N1) neuraminidase inhibitors from glycyrrhiza inflata. Bioorg Med Chem Lett 21:294–298. doi:10.1016/j.bmcl.2010.11.016

    Article  CAS  PubMed  Google Scholar 

  40. Ivachtchenko AV, Ivanenkov YA, Mitkin OD, Yamanushkin PM, Bichko VV, Leneva IA, Borisova OV (2013) A novel influenza virus neuraminidase inhibitor AV5027. Antiviral Res 100:698–708. doi:10.1016/j.antiviral.2013.10.008

    Article  CAS  PubMed  Google Scholar 

  41. Jang YJ, Achary R, Lee HW, Lee HJ, Lee CK, Han SB, Jung YS, Kang NS, Kim P, Kim M (2014) Synthesis and anti-influenza virus activity of 4-oxo- or thioxo-4,5-dihydrofuro[3,4-C]pyridin-3(1H)-ones. Antiviral Res 107:66–75. doi:10.1016/j.antiviral.2014.04.013

    Article  CAS  PubMed  Google Scholar 

  42. Kati WM, Montgomery D, Carrick R, Gubareva L, Maring C, McDaniel K, Steffy K, Molla A, Hayden F, Kempf D, Kohlbrenner W (2002) In vitro characterization of A-315675, a highly potent inhibitor of A and B strain influenza virus neuraminidases and influenza virus replication. Antimicrob Agents Chemother 46:1014–1021. doi:10.1128/AAC.46.4.1014-1021.2002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mohan S, Kerry PS, Bance N, Niikura M, Pinto BM (2014) Serendipitous discovery of a potent influenza virus A neuraminidase inhibitor. Angew Chem Int Ed Engl 53:1076–1080. doi:10.1002/anie.201308142

    Article  CAS  PubMed  Google Scholar 

  44. Xie Y, Huang B, Yu K, Shi F, Liu T, Xu W (2013) Caffeic acid derivatives: a new type of influenza neuraminidase inhibitors. Bioorg Med Chem Lett 23:3556–3560. doi:10.1016/j.bmcl.2013.04.033

    Article  CAS  PubMed  Google Scholar 

  45. Xie Y, Huang B, Yu K, Xu W (2013) Further discovery of caffeic acid derivatives as novel influenza neuraminidase inhibitors. Bioorg Med Chem 21:7715–7723. doi:10.1016/j.bmc.2013.10.020

    Article  CAS  PubMed  Google Scholar 

  46. Discovery Studio (2014) Version 4.0, Accelrys Inc., San Diego. http://accelrys.com

  47. Molecular Operating Environment (MOE) (2010) Version 2010.10. Chemical Computing Group Inc., Montreal. http://www.chemcomp.com

  48. ADRIANA.Code (2011) Version 2.2.6. Molecular Networks Inc., Erlangen. http://www.molecular-networks.com

  49. SPSS Statistics (2008) Version 17.0. IBM Inc., New York. http://www.ibm.com

  50. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999. doi:10.1109/72.788640

    Article  CAS  PubMed  Google Scholar 

  51. Ma XH, Wang R, Yang SY, Li ZR, Xue Y, Wei YC, Low BC, Chen YZ (2008) Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J Chem Inf Model 48:1227–1237. doi:10.1021/ci800022e

    Article  CAS  PubMed  Google Scholar 

  52. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567. doi:10.1038/nbt1206-1565

    Article  CAS  PubMed  Google Scholar 

  53. Chang CC, Lin CJ (2001) LIBSVM: a library for SVM. Software. http://www.csie.ntu.edu.tw/~cjlin/libsvm

  54. Chen L, Li Y, Zhao Q, Peng H, Hou T (2011) ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and Naïve Bayesian classification techniques. Mol Pharm 8:889–900. doi:10.1021/mp100465q

    Article  CAS  PubMed  Google Scholar 

  55. Chang CY, Hsu MT, Esposito EX, Tseng YJ (2013) Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods. J Chem Inf Model 53:958–971. doi:10.1021/ci4000536

    Article  CAS  PubMed  Google Scholar 

  56. Li Q, Wang Y, Bryant SH (2009) A novel method for mining highly imbalanced high-throughput screening data in Pubchem. Bioinformatics 25:3310–3316. doi:10.1093/bioinformatics/btp589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kramer C, Beck B, Clark T (2010) Insolubility classification with accurate prediction probabilities using a metaclassifier. J Chem Inf Model 50:404–414. doi:10.1021/ci900377e

    Article  CAS  PubMed  Google Scholar 

  58. Schultes S, Kooistra AJ, Vischer HF, Nijmeijer S, Haaksma EE, Leurs R, de Esch IJ, de Graaf C (2015) Combinatorial consensus scoring for ligand-based virtual fragment screening: a comparative case study for Serotonin 5-HT3A, Histamine H1, and Histamine H4 Receptors. J Chem Inf Model 55:1030–1044. doi:10.1021/ci500694c

    Article  CAS  PubMed  Google Scholar 

  59. Mohan S, McAtamney S, Haselhorst T, von Itzstein M, Pinto BM (2010) Carbocycles related to oseltamivir as influenza virus group-1-specific neuraminidase inhibitors. Binding to N1 enzymes in the context of virus-like particles. J Med Chem 53:7377–7391. doi:10.1021/jm100822f

    Article  CAS  PubMed  Google Scholar 

  60. Wen WH, Wang SY, Tsai KC, Cheng YS, Yang AS, Fang JM, Wong CH (2010) Analogs of zanamivir with modified C4-substituents as the inhibitors against the group-1 neuraminidases of influenza viruses. Bioorg Med Chem 18:4074–4084. doi:10.1016/j.bmc.2010.04.010

  61. Rudrawar S, Kerry PS, Rameix-Welti MA, Maggioni A, Dyason JC, Rose FJ, van der Werf S, Thomson RJ, Naffakh N, Russell RJ, von Itzstein M (2012) Synthesis and evaluation of novel 3-C-alkylated-Neu5Ac2en derivatives as probes of influenza virus sialidase 150-loop flexibility. Org Biomol Chem 10:8628–8639. doi:10.1039/c2ob25627d

    Article  CAS  PubMed  Google Scholar 

  62. Russell RJ, Haire LF, Stevens DJ, Collins PJ, Lin YP, Blackburn GM, Hay AJ, Gamblin SJ, Skehel JJ (2006) The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design. Nature 443:45–49. doi:10.1038/nature05114

    Article  CAS  PubMed  Google Scholar 

  63. Greenway KT, LeGresley EB, Pinto BM (2013) The influence of 150-cavity binders on the dynamics of influenza A neuraminidases as revealed by molecular dynamics simulations and combined clustering. PLoS One 8:e59873. doi:10.1371/journal.pone.0059873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wang P, Zhang JZ (2010) Selective binding of antiinfluenza drugs and their analogues to ’open’ and ’closed’ conformations of H5N1 neuraminidase. J Phys Chem B 114:12958–12964. doi:10.1021/jp1030224

    Article  CAS  PubMed  Google Scholar 

  65. Wu Y, Qin G, Gao F, Liu Y, Vavricka CJ, Qi J, Jiang H, Yu K, Gao GF (2013) Induced opening of influenza virus neuraminidase N2 150-loop suggests an important role in inhibitor binding. Sci Rep 3:1551–1558. doi:10.1038/srep01551

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by Beijing Natural Science Foundation (7152103), the National Great Science and Technology Projects (2012ZX09301002-2013HXW-11, 2013ZX09508104001002, 2014ZX09507003-002), and the 863 Project (2014AA021101).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ai-Lin Liu.

Ethics declarations

Conflicts of interest

The authors declare no competing financial interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (docx 1581 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lian, W., Fang, J., Li, C. et al. Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models. Mol Divers 20, 439–451 (2016). https://doi.org/10.1007/s11030-015-9641-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-015-9641-z

Keywords

Navigation