Advertisement

Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study

  • Sathya Babu
  • Santhosh Kumar Nagarajan
  • Thirumurthy MadhavanEmail author
Original Article
  • 88 Downloads

Abstract

JAK2 plays a critical role in JAK/STAT signaling pathway and in patho-mechanism of myeloproliferative disorders and autoimmune diseases. Thus, effective JAK2 inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. In this work, 3D-QSAR study was performed on a series of 1-amino-5H-pyrido-indole-4-carboxamide derivatives as JAK2 inhibitors to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) models with three different alignment methods. Among the different alignment methods, ligand-based (CoMFA: q2 = 0.676, r2 = 0.979; CoMSIA: q2 = 0.700, r2 = 0.953) and pharmacophore-based alignment (CoMFA: q2 = 0.710, r2 = 0.982; CoMSIA: q2 = 0.686, r2 = 0.960) has produced better statistical results when compared to receptor-based alignment (CoMFA: q2 = 0.507, r2 = 0.979; CoMSIA: q2 = 0.544, r2 = 0.917). Statistical parameters indicated that data are well fitted and have high predictive ability. The presence of electrostatic and hydrophobic field is highly desirable for potent inhibitory activity, and the steric field plays a minor role in modulating the activity. The contour analysis indicates ARG980, ASN981, ASP939 and LEU937 have more possibility of interacting with bulky, hydrophobic groups in pyrido and positive and negative groups in pyrazole ring. Based on our findings, we have designed sixteen molecules and predicted its activity and drug-like properties. Subsequently, molecular docking, molecular dynamics and DFT calculations were performed to evaluate its potency.

Graphical abstract

Keywords

JAK2 3D-QSAR CoMFA CoMSIA Molecular dynamics DFT 

Notes

Acknowledgements

This research was supported by Start-Up Research Grant for Young Scientist (SB/YS/LS-128/2013), funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India. Author SB thanks CSIR, New Delhi, India for providing Senior Research Fellowship (SRF).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11030_2018_9913_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1156 kb)

References

  1. 1.
    Leonard WJ, O’Shea JJ (1998) Jaks and STATs: biological implications. Annu Rev Immunol 16:293–322.  https://doi.org/10.1146/annurev.immunol.16.1.293 CrossRefGoogle Scholar
  2. 2.
    Laurence A, Pesu M, Silvennoinen O, O’Shea J (2012) JAK kinases in health and disease: an update. Open Rheumatol J 6:232–244.  https://doi.org/10.2174/1874312901206010232 CrossRefGoogle Scholar
  3. 3.
    Vainchenker W, Constantinescu SN (2013) JAK/STAT signaling in hematological malignancies. Oncogene 32:2601–2613.  https://doi.org/10.1038/onc.2012.347 CrossRefGoogle Scholar
  4. 4.
    Quintas-Cardama A, Vertovsek S (2011) New JAK2 inhibitors for myeloproliferative neoplasms. Expert Opin Invest Drugs 20:961–972.  https://doi.org/10.1517/13543784.2011.579560 CrossRefGoogle Scholar
  5. 5.
    Williams NK, Bamert RS, Patel O, Wang C, Walden PM, Wilks AF, Fantino E, Rossjohn J, Lucet IS (2009) Dissecting specificity in the Janus kinases: the structures of JAK-specific inhibitors complexed to the JAK1 and JAK2 protein tyrosine kinase domains. J Mol Biol 387:219–232.  https://doi.org/10.1016/j.jmb.2009.01.041 CrossRefGoogle Scholar
  6. 6.
    Vrontaki E, Melagraki G, Afantitis A, Mavromoustakos T, Kollias G (2017) Searching for novel Janus kinase-2 inhibitors using a combination of pharmacophore modeling, 3D-QSAR studies and virtual screening. Mini Rev Med Chem 17:268–294.  https://doi.org/10.2174/1389557516666160919163930 CrossRefGoogle Scholar
  7. 7.
    Kisseleva T, Bhattacharya S, Braunstein J, Schindler CW (2002) Signaling through the JAK/STAT pathway, recent advances and future challenges. Gene 285:1–24.  https://doi.org/10.1016/S0378-1119(02)00398-0 CrossRefGoogle Scholar
  8. 8.
    Lindauer K, Loerting T, Liedl KR, Kroemer RT (2001) Prediction of the structure of human Janus kinase 2 (JAK2) comprising the two carboxy-terminal domains reveals a mechanism for autoregulation. Protein Eng 14:27–37.  https://doi.org/10.1093/protein/14.1.27 CrossRefGoogle Scholar
  9. 9.
    Remy I, Wilson IA, Michnick SW (1990) Erythropoietin receptor activation by a ligand-induced conformation change. Science 283:990–993.  https://doi.org/10.2307/2897727 CrossRefGoogle Scholar
  10. 10.
    Ishihara K, Hirano T (2002) Molecular basis of the cell specificity of cytokine action. Biochim Biophys Acta 1592:281–296.  https://doi.org/10.1016/S0167-4889(02)00321-X CrossRefGoogle Scholar
  11. 11.
    Burns CJ, Bourke DG, Andrau L, Bu X, Charman SA, Donohue AC, Fantino E, Farrugia M, Feutrill JT, Joffe M, Kling MR, Kurek M, Nero TL, Nguyen T, Palmer JT, Phillips I, Shackleford DM, Sikanyika H, Styles M, Su S, Treutlein H, Zeng J, Wilks AF (2009) Phenylaminopyrimidines as inhibitors of Janus kinases (JAKs). Bioorg Med Chem Lett 19:5887–5892.  https://doi.org/10.1016/j.bmcl.2009.08.071 CrossRefGoogle Scholar
  12. 12.
    Chen E, Staudt LM, Green AR (2012) Janus kinase deregulation in leukemia and lymphoma. Immunity 36:529–541.  https://doi.org/10.1016/j.immuni.2012.03.017 CrossRefGoogle Scholar
  13. 13.
    Ghoreschi K, Laurence A, O’Shea JJ (2009) Janus kinases in immune cell signaling. Immunol Rev 228:2730–2787.  https://doi.org/10.1111/j.1600-065X.2008.00754.x CrossRefGoogle Scholar
  14. 14.
    Rawlings JS, Rosler KM, Harrison DA (2004) The JAK/STAT signaling pathway. J Cell Sci 117:1281–1283.  https://doi.org/10.1242/jcs.00963 CrossRefGoogle Scholar
  15. 15.
    Timofeeva OA, Tarasova NI (2012) Alternative ways of modulating JAK-STAT pathway: looking beyond phosphorylation. JAKSTAT 1:274–284.  https://doi.org/10.4161/jkst.22313 Google Scholar
  16. 16.
    Aaronson DS, Horvath CM (2002) A road map for those who don’t know JAK-STAT. Science 296:1653–1655.  https://doi.org/10.1126/science.1071545 CrossRefGoogle Scholar
  17. 17.
    Imada K, Leonard WJ (2000) The Jak-STAT pathway. Mol Immunol 37:1–11.  https://doi.org/10.1016/S0161-5890(00)00018-3 CrossRefGoogle Scholar
  18. 18.
    Wu W, Sun XH (2011) Janus kinase 3: the controller and the controlled. Acta Biochim Biophys Sin 44:187–196.  https://doi.org/10.1093/abbs/gmr105 CrossRefGoogle Scholar
  19. 19.
    Yeh TC, Pellegrini S (1999) The Janus kinase family of protein tyrosine kinases and their role in signaling. Cell Mol Life Sci 55:1523–1534.  https://doi.org/10.1007/s000180050392 CrossRefGoogle Scholar
  20. 20.
    Yamaoka K, Saharinen P, Pesu M, Holt VE, Silvennoinen O, O’Shea JJ (2004) The Janus kinases (Jaks). Genome Biol 5:253–258.  https://doi.org/10.1186/gb-2004-5-12-253 CrossRefGoogle Scholar
  21. 21.
    Lucet IS, Fantino E, Styles M, Bamert R, Patel O, Broughton SE, Walter M, Burns CJ, Treutlein H, Wilks AF, Rossjohn J (2003) The structural basis of Janus kinase 2 inhibition by a potent and specific pan-Janus kinase inhibitor. Blood 107:176–183.  https://doi.org/10.1182/blood-2005-06-2413 CrossRefGoogle Scholar
  22. 22.
    Sayyah J, Sayeski PP (2009) Jak2 inhibitors: rationale and role as therapeutic agents in hematologic malignancies. Curr Oncol Rep 11:117–124.  https://doi.org/10.1007/s11912-009-0018-2 CrossRefGoogle Scholar
  23. 23.
    Mascarenhas J, Mughal TI, Verstovsek S (2012) Biology and clinical management of myeloproliferative neoplasms and development of the JAK inhibitor Ruxolitinib. Curr Med Chem 19:4399–4413.  https://doi.org/10.2174/092986712803251511 CrossRefGoogle Scholar
  24. 24.
    Delhommeau F, Pisani DF, James C, Casadevall N, Constantinescu S, Vainchenker W (2006) Oncogenic mechanisms in myeloproliferative disorders. Cell Mol Life Sci 63:2939–2953.  https://doi.org/10.1007/s00018-006-6272-7 CrossRefGoogle Scholar
  25. 25.
    Karoline G, Iris B, Claude H (2013) JAK2 mutants (e.g., JAK2V617F) and their importance as drug targets in myeloproliferative neoplasm. JAK-STAT 2.  https://doi.org/10.4161/jkst.25025
  26. 26.
    James C (2008) The JAK2V617F mutation in polycythemia vera and other myeloproliferative disorders: one mutation for three diseases? Hematol Am Soc Hematol Educ Progr 2008:69–75.  https://doi.org/10.1182/asheducation-2008.1.69 CrossRefGoogle Scholar
  27. 27.
    Falanga A, Marchetti M, Vignoli A, Balducci D, Russo L, Guerini V, Barbui T (2007) V617F JAK-2 mutation in patients with essential thrombocythemia: relation to platelet, granulocyte, and plasma hemostatic and inflammatory molecules. Exp Hematol 35:702–711.  https://doi.org/10.1016/j.exphem.2007.01.053 CrossRefGoogle Scholar
  28. 28.
    Matthews DJ, Gerritsen ME (2010) Targeting protein kinases for cancer therapy. Wiley, New York.  https://doi.org/10.1002/9780470555293 CrossRefGoogle Scholar
  29. 29.
    Lippert E, Boissinot M, Kralovics R, Girodon F, Dobo I, Praloran V, Boiret-Dupre N, Skoda RC, Hermouet S (2006) The JAK2- V617F mutation is frequently present at diagnosis in patients with essential thrombocythemia and polycythemia vera. Blood 108:1865–1867.  https://doi.org/10.1182/blood-2006-01-013540 CrossRefGoogle Scholar
  30. 30.
    Levine RL, Wadleigh M, Cools J, Ebert BL, Wernig G, Huntly BJP, Boggon TJ, Wlodarska I, Lark JJ, Moore S, Adelsperger J, Koo S, Lee JC, Gabriel S, Mercher T, D’Andrea A, Froehling S, Doehner K, Marynen P, Vandenberghe P, Mesa RA, Tefferi A, Griffin JD, Eck MJ, Sellers WR, Meyerson M, Golub TR, Lee SJ, Gilliland DG (2005) Activating mutation in the tyrosine kinase JAK2 in polycythemia vera, essential thrombocythemia, and myeloid metaplasia with myelofibrosis. Cancer Cell 7:387–397.  https://doi.org/10.1016/j.ccr.2005.03.023 CrossRefGoogle Scholar
  31. 31.
    Baxter EJ, Scott LM, Campbell PJ, East C, Fourouclas N, Swanton S, Vassiliou GS, Bench AJ, Boyd EM, Curtin N, Scott MA, Erber WN, Green AR (2005) Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet 365:1054–1061.  https://doi.org/10.1016/S0140-6736(05)71142-9 CrossRefGoogle Scholar
  32. 32.
    Jones AV, Kreil S, Zoi K, Waghorn K, Curtis C, Zhang L, Score J, Seear R, Chase AJ, Grand FH, White H, Zoi C, Loukopoulos D, Terpos E, Vervessou EC, Schultheis B, Emig M, Ernst T, Lengfelder E, Hehlmann R, Hochhaus A, Oscier D, Silver RT, Reiter A, Cross NC (2005) Widespread occurrence of the JAK2 V617F mutation in chronic myeloproliferative disorders. Blood 106:2162–2168.  https://doi.org/10.1182/blood-2005-03-1320 CrossRefGoogle Scholar
  33. 33.
    Clark JD, Flanagan ME, Telliez JB (2014) Discovery and development of Janus kinase (JAK) inhibitors for inflammatory diseases. J Med Chem 57:5023–5038.  https://doi.org/10.1021/jm401490p CrossRefGoogle Scholar
  34. 34.
    O’Shea JJ, Kontzias A, Yamaoka K, Tanaka Y, Laurence A (2013) Janus kinase inhibitors in autoimmune diseases. Ann Rheum Dis 72:ii111–ii115.  https://doi.org/10.1136/annrheumdis-2012-202576 CrossRefGoogle Scholar
  35. 35.
    Quintas-Cardama A, Kantarjian H, Cortes J, Verstovsek S (2011) Janus kinase inhibitors for the treatment of myeloproliferative neoplasias and beyond. Nat Rev Drug Discov 10:127–140.  https://doi.org/10.1038/nrd3264 CrossRefGoogle Scholar
  36. 36.
    Tan SH, Nevalainen MT (2008) Signal transducer and activator of transcription 5A/B in prostate and breast cancers. Endocr Relat Cancer 15:367–390.  https://doi.org/10.1677/ERC-08-0013 CrossRefGoogle Scholar
  37. 37.
    Dearden JC (2016) The history and development of quantitative structure-activity relationships (QSARs). IJQSPR 1:1–44.  https://doi.org/10.4018/IJQSPR.2016010101 Google Scholar
  38. 38.
    Itteboina R, Ballu S, Sivan SK, Manga V (2016) Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors. Comput Biol Chem 64:33–46.  https://doi.org/10.1016/j.compbiolchem.2016.04.009 CrossRefGoogle Scholar
  39. 39.
    Jasuja H, Chadha N, Kaur M, Silakari O (2014) Dual inhibitors of Janus kinase 2 and 3 (JAK2/3): designing by pharmacophore- and docking-based virtual screening approach. Mol Divers 18:253–267.  https://doi.org/10.1007/s11030-013-9497-z CrossRefGoogle Scholar
  40. 40.
    Dhanachandra Singh Kh, Karthikeyan M, Kirubakaran P, Nagamani S (2011) Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. J Mol Graph Model 30:186–197.  https://doi.org/10.1016/j.jmgm.2011.07.004 CrossRefGoogle Scholar
  41. 41.
    Singh KhD, Naveena Q, Karthikeyan M (2014) Jak2 inhibitor–a jackpot for pharmaceutical industries: a comprehensive computational method in the discovery of new potent Jak2 inhibitors. Mol BioSyst 10:2146–2159.  https://doi.org/10.1039/c4mb00071d CrossRefGoogle Scholar
  42. 42.
    Gade DR, Kunala P, Raavi D, Reddy PK, Prasad RV (2015) Structural insights of JAK2 inhibitors: pharmacophore modeling and ligand-based 3D-QSAR studies of pyrido-indole derivatives. J Recept Signal Transduct Res 35:189–201.  https://doi.org/10.3109/10799893.2014.948556 CrossRefGoogle Scholar
  43. 43.
    Chekkara R, Susithra E, Kandakatla N, Gorla VR, Tenkayala SR (2014) Pharmacophore generation and atom-based 3D-QSAR analysis of substituted aromatic bicyclic compounds containing pyrimidine and pyridine rings as Janus kinase 2 (JAK2) inhibitors. J Chem Pharm Res 6:1146–1152Google Scholar
  44. 44.
    Wang JL, Cheng LP, Wang TC, Deng W, Wu FH (2017) Molecular modeling study of CP-690550 derivatives as JAK3 kinase inhibitors through combined 3D-QSAR, molecular docking, and dynamics simulation techniques. J Mol Graph Model 72:178–186.  https://doi.org/10.1016/j.jmgm.2016.12.020 CrossRefGoogle Scholar
  45. 45.
    Anand B, Pavithra KB, Seung JC (2017) 3D-QSAR, docking, molecular dynamics simulation and free energy calculation studies of some pyrimidine derivatives as novel JAK3 inhibitors. Arab J Chem.  https://doi.org/10.1016/j.arabjc.2017.09.009 Google Scholar
  46. 46.
    Rajeswari M, Santhi N, Bhuvaneswari V (2014) Pharmacophore and virtual screening of JAK3 inhibitors. Bioinformation 10:157–163.  https://doi.org/10.6026/97320630010157 CrossRefGoogle Scholar
  47. 47.
    Lim J, Taoka B, Otte RD, Spencer K, Dinsmore CJ, Altman MD, Chan G, Rosenstein C, Sharma S, Su HP, Szewczak AA, Xu L, Yin H, Zugay-Murphy J, Marshall CG, Young JR (2011) Discovery of 1-Amino-5H-pyrido[4,3-b]indol-4-carboxamide inhibitors of Janus kinase 2 (JAK2) for the treatment of myeloproliferative disorders. J Med Chem 54:7334–7349.  https://doi.org/10.1021/jm200909u CrossRefGoogle Scholar
  48. 48.
    Powell MJD (1977) Restart procedures for conjugate gradient method. Math Progr 12:241–254.  https://doi.org/10.1007/BF01593790 CrossRefGoogle Scholar
  49. 49.
    Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228.  https://doi.org/10.1016/0040-4020(80)80168-2 CrossRefGoogle Scholar
  50. 50.
    SYBYLX -2.1Software, Tripos Associates Inc, St. LouisGoogle Scholar
  51. 51.
    Cho SJ, Tropsha A (1995) Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results. J Med Chem 38:1060–1066.  https://doi.org/10.1021/jm00007a003 CrossRefGoogle Scholar
  52. 52.
    Jones G, Willett P, Glen RC (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 9:532–549.  https://doi.org/10.1007/BF00124324 CrossRefGoogle Scholar
  53. 53.
    Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46:499–511.  https://doi.org/10.1021/jm020406h CrossRefGoogle Scholar
  54. 54.
    Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7:407–420.  https://doi.org/10.2174/138920306778559395 CrossRefGoogle Scholar
  55. 55.
    Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis CoMSIA of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146.  https://doi.org/10.1021/jm00050a010 CrossRefGoogle Scholar
  56. 56.
    Wold S, Sjostrom M, Eriksson L (2011) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130.  https://doi.org/10.1016/S0169-7439(01)00155-1 CrossRefGoogle Scholar
  57. 57.
    Cramer RD (1993) Partial least squares (PLS): its strength and limitations. Perspect Drug Discov Des 1:269–278.  https://doi.org/10.1007/BF02174528 CrossRefGoogle Scholar
  58. 58.
    Geladi P, Xie YL, Polissar A, Hopke P (1998) Regression on parameters from three way decomposition. J Chemom 12:337–354.  https://doi.org/10.1002/(SICI)1099-128X(199809/10)12:5<337::AID-CEM517>3.0.CO;2-1 CrossRefGoogle Scholar
  59. 59.
    Wold S, Ruhe A, Wold H, Dunn WJ (1984) The collinearity problem in linear regression. the partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5:735–743.  https://doi.org/10.1137/0905052 CrossRefGoogle Scholar
  60. 60.
    Roy K, Supratik K, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29.  https://doi.org/10.1016/j.chemolab.2015.04.013 CrossRefGoogle Scholar
  61. 61.
    Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Sys 152:18–33.  https://doi.org/10.1016/j.chemolab.2016.01.008 CrossRefGoogle Scholar
  62. 62.
    Rücker C, Rücker G, Meringer M (2007) y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–2357.  https://doi.org/10.1021/ci700157b CrossRefGoogle Scholar
  63. 63.
  64. 64.
    Desmond Molecular Dynamics System, version 3.6, D. E. Shaw Research, New York, NY, 2013. Maestro- Desmond Interoperability Tools, version 3.6, Schrodinger, New York, NYGoogle Scholar
  65. 65.
    Kaminski G, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparameterization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487.  https://doi.org/10.1021/jp003919d CrossRefGoogle Scholar
  66. 66.
    Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652.  https://doi.org/10.1063/1.464913 CrossRefGoogle Scholar
  67. 67.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell A P, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 16, Revision B.01, Gaussian Inc., Wallingford CTGoogle Scholar
  68. 68.
    Lee C, Yang W, Parr RG (1988) Development of the Colle–Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785–789.  https://doi.org/10.1103/PhysRevB.37.785 CrossRefGoogle Scholar
  69. 69.
    Domingo LR, Ríos-Gutiérrez M, Pérez P (2016) Applications of the conceptual density functional theory indices to organic chemistry reactivity. Molecules 21:748–770.  https://doi.org/10.3390/molecules21060748 CrossRefGoogle Scholar
  70. 70.
    Pissot-Soldermann C, Gerspacher M, Furet P, Gaul C, Holzer P, McCarthy C, Radimerski T, Regnier CH, Baffert F, Drueckes P, Tavares GA, Vangrevelinghe E, Blasco F, Ottaviani G, Ossola F, Scesa J, Reetz J (2010) Discovery and SAR of potent, orally available 2,8-diaryl-quinoxalines as a new class of JAK2 inhibitors. Bioorg Med Chem Lett 20:2609–2613.  https://doi.org/10.1016/j.bmcl.2010.02.056 CrossRefGoogle Scholar
  71. 71.
    Golbraikh A, Tropsha A (2002) Beware of q2. J Mol Graph Model 20:269–276.  https://doi.org/10.1016/S1093-3263(01)00123-1 CrossRefGoogle Scholar
  72. 72.
    Hart AC, Schroeder GM, Wan H, Grebinski J, Inghrim J, Kempson J, Guo J, Pitts WJ, Tokarski JS, Sack JS, Khan JA, Lippy J, Lorenzi MV, You D, McDevitt T, Vuppugalla R, Zhang Y, Lombardo LJ, Trainor GL, Purandare AV (2015) Structure-based design of selective Janus kinase 2 Imidazo[4,5d]pyrrolo[2,3b] pyridine Inhibitors. ACS Med Chem Lett 6:845–849.  https://doi.org/10.1021/acsmedchemlett.5b00225 CrossRefGoogle Scholar
  73. 73.
    Schenkel LB, Huang X, Cheng A, Deak HL, Doherty E, Emkey R, Gu Y, Gunaydin H, Kim JL, Lee J, Loberg R, Olivieri P, Pistillo J, Tang J, Wan Q, Wang HL, Wang SW, Wells MC, Wu B, Yu V, Liu L, Geuns-Meyer S (2011) Discovery of potent and highly selective thienopyridine Janus kinase 2 inhibitors. J Med Chem 54:8440–8450.  https://doi.org/10.1021/jm200911r CrossRefGoogle Scholar
  74. 74.
    Hanan EJ, Abbema AV, Barrett K, Blair WS, Blaney J, Chang C, Eigenbrot C, Flynn S, Gibbons P, Hurley CA, Kenny JR, Kulagowski J, Lee L, Magnuson SR, Morris C, Murray J, Pastor RM, Rawson T, Siu M, Ultsch M, Zhou A, Sampath D, Lyssikatos JP (2012) Discovery of potent and selective pyrazolopyrimidine Janus kinase 2 inhibitors. J Med Chem 55:10090–10107.  https://doi.org/10.1021/jm3012239 CrossRefGoogle Scholar
  75. 75.
    Parr RG, Donnelly RA, Levy M, Palke WE (1978) Electronegativity—density functional viewpoint. J Chem Phys 68:3801–3807.  https://doi.org/10.1063/1.436185 CrossRefGoogle Scholar
  76. 76.
    Mert BD, Mert ME, Kardas G, Yazici B (2011) Experimental and theoretical investigation of 3-amino-1, 2, 4-triazole-5-thiol as a corrosion inhibitor for carbon steel in HCl medium. Corros Sci 53:4265–4272.  https://doi.org/10.1016/j.corsci.2011.08.038 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computational Biology Lab, Department of Genetic Engineering, School of BioengineeringSRM Institute of Science and TechnologyChennaiIndia

Personalised recommendations