Structural Chemistry

, Volume 30, Issue 6, pp 2347–2368 | Cite as

Molecular docking and receptor-based QASR studies on pyrimidine derivatives as potential phosphodiesterase 10A inhibitors

  • Elham Gholami RostamiEmail author
  • Mohammad Hossein Fatemi
Original Research


In the present work, molecular docking methodology in combination with quantitative structure–activity relationship (QSAR) was employed to predict the inhibition activity of 87 structurally diverse pyrimidine-based derivatives as phosphodiestrae10A (PDE10A) inhibitors due to their potential in the treatment of schizophrenia. In this method, compounds in their preferred enzyme-docked conformations were utilized to derive interaction-based quantitative descriptors in order to explain reported PDE10A inhibitory activities. Multiple linear regression (MLR), artificial neural network (ANN), and least square support vector regression (LS-SVR) were exploited to developing the structure-based quantitative structure–activity relationship models. Among these models, LS-SVR model showed more satisfactory statistical parameters with regard to both internal (Rtrain = 0.951, Q2 = 0.804, RMSEtrain = 0.494) and external validation (Rtest = 0.941, RMSEtest = 0.549) test results. Information from the most relevant descriptors suggests that incorporating steric effect, electronegativity, and the number of substituted aromatic carbon correlate the activity with structural features of the studied compounds. Molecular docking analysis of the most potent inhibitor explored that hydrogen bond formation and hydrophobicity participated in the binding interaction of PDE10A complex active pocket which these findings are in line with those obtained from QSAR model. The reliability assessment of compounds predictions was checked by model applicability domain (AD) analysis.


Molecular docking Quantitative structure–activity relationship Phosphodiestrae10A Pyrimidine derivatives Schizophrenia 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Joseph BC (2007) Biochemistry and physiology of cyclic nucleotide phosphodiesterases: essential components in cyclic nucleotide signaling. Annu Rev Biochem 76:481–511Google Scholar
  2. 2.
    Bender AT, Beavo JA (2006) Cyclic nucleotide phosphodiesterases: molecular regulation to clinical use. Physiol Rev 58(3):488–520Google Scholar
  3. 3.
    Beavo JA (1995) Cyclic nucleotide phosphodiesterases: functional implications of multiple isoforms. Physiol Rev 75(4):725–748PubMedGoogle Scholar
  4. 4.
    Soderling SH, Bayuga SJ, Beavo JA (1999) Isolation and characterization of a dual-substrate phosphodiesterase gene family: PDE10A. PNAS 96(12):7071–7076PubMedGoogle Scholar
  5. 5.
    Fujishige K, Kotera J, Michibata H, Yuasa K, Takebayashi S-I, Okumura K, Omori K (1999) Cloning and characterization of a novel human phosphodiesterase that hydrolyzes both cAMP and cGMP (PDE10A). J Biol Chem 274(26):18438–18445PubMedGoogle Scholar
  6. 6.
    Seeger TF, Bartlett B, Coskran TM, Culp JS, James LC, Krull DL, Lanfear J, Ryan AM, Schmidt CJ, Strick CA (2003) Immunohistochemical localization of PDE10A in the rat brain. Brain Res 985(2):113–126PubMedGoogle Scholar
  7. 7.
    Menniti FS, Faraci WS, Schmidt CJ (2006) Phosphodiesterases in the CNS: targets for drug development. Nat Rev Drug Discov 5(8):660PubMedGoogle Scholar
  8. 8.
    Sorg C, Manoliu A, Neufang S, Myers N, Peters H, Schwerthöffer D, Scherr M, Mühlau M, Zimmer C, Drzezga A (2012) Increased intrinsic brain activity in the striatum reflects symptom dimensions in schizophrenia. Schizophrenia Bull 39(2):387–395Google Scholar
  9. 9.
    Tuttle JB, Kormos BL (2014) The use of PDE10A and PDE9 inhibitors for treating schizophrenia. In: Small molecule therapeutics for schizophrenia. Springer, Cham. Topics Med Chem 13:255-316Google Scholar
  10. 10.
    Menniti FS, Chappie TA, Humphrey JM, Schmidt CJ (2007) Phosphodiesterase 10A inhibitors: a novel approach to the treatment of the symptoms of schizophrenia. Curr Opin Investig Drugs 8(1):54–59PubMedGoogle Scholar
  11. 11.
    Siuciak JA, Chapin DS, Harms JF, Lebel LA, McCarthy SA, Chambers L, Shrikhande A, Wong S, Menniti FS, Schmidt CJ (2006) Inhibition of the striatum-enriched phosphodiesterase PDE10A: a novel approach to the treatment of psychosis. Neuropharmacology 51(2):386–396PubMedGoogle Scholar
  12. 12.
    Bardin L, Auclair A, Kleven MS, Prinssen EP, Koek W, Newman-Tancredi A, Depoortere R (2007) Pharmacological profiles in rats of novel antipsychotics with combined dopamine D2/serotonin 5-HT1A activity: comparison with typical and atypical conventional antipsychotics. Behav Pharmacol 18(2):103–118PubMedGoogle Scholar
  13. 13.
    Seeman P (2006) Targeting the dopamine D2 receptor in schizophrenia. Expert Opin Ther Targets 10(4):515–531PubMedGoogle Scholar
  14. 14.
    Kennedy JL, Altar CA, Taylor DL, Degtiar I, Hornberger JC (2014) The social and economic burden of treatment-resistant schizophrenia: a systematic literature review. Int Clin Psychopharm 29(2):63–76Google Scholar
  15. 15.
    Muly C (2002) Signal transduction abnormalities in schizophrenia: the cAMP system. Psychopharmacol Bull 36(4):92–105PubMedGoogle Scholar
  16. 16.
    Aparoy P, Kumar Reddy K, Reddanna P (2012) Structure and ligand based drug design strategies in the development of novel 5-LOX inhibitors. Curr Med Chem 19(22):3763–3778PubMedPubMedCentralGoogle Scholar
  17. 17.
    Bacilieri M, Moro S (2006) Ligand-based drug design methodologies in drug discovery process: an overview. Curr Drug Discov Technol 3(3):155–165PubMedGoogle Scholar
  18. 18.
    Kalyaanamoorthy S, Chen Y-PP (2011) Structure-based drug design to augment hit discovery. Drug Discov Today 16(17–18):831–839PubMedGoogle Scholar
  19. 19.
    Huang H-J, Yu HW, Chen C-Y, Hsu C-H, Chen H-Y, Lee K-J, Tsai F-J, Chen CY-C (2010) Current developments of computer-aided drug design. J Taiwan Inst Chem E 41(6):623–635Google Scholar
  20. 20.
    Sepehri S, Gharagani S, Saghaie L, Aghasadeghi MR, Fassihi A (2015) QSAR and docking studies of some 1, 2, 3, 4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity. Med Chem Res 24(4):1707–1724Google Scholar
  21. 21.
    Zhang S (2011) Computer-aided drug discovery and development. In: Drug Design and Discovery. Springer, Humana Press, New York. pp 23–38Google Scholar
  22. 22.
    Ferreira L, dos Santos R, Oliva G, Andricopulo A (2015) Molecular docking and structure-based drug design strategies. Molecules 20(7):13384–13421PubMedPubMedCentralGoogle Scholar
  23. 23.
    Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66(1):334–395PubMedPubMedCentralGoogle Scholar
  24. 24.
    Wadood A, Ahmed N, Shah L, Ahmad A, Hassan H, Shams S (2013) In-silico drug design: an approach which revolutionarised the drug discovery process. OA Drug Des Deliv 1(1):3–7Google Scholar
  25. 25.
    Kulkarni SS, Patel MR, Talele TT (2008) CoMFA and HQSAR studies on 6, 7-dimethoxy-4-pyrrolidylquinazoline derivatives as phosphodiesterase10A inhibitors. Bioorgan Med Chem 16(7):3675–3686Google Scholar
  26. 26.
    Liu Y, Lu X, Xue T, Hu S, Zhang H (2014) Receptor and ligand-based 3D-QSAR study on a series of pyrazines/pyrrolidylquinazolines as inhibitors of PDE10A enzyme. Med Chem Res 23(2):775–789Google Scholar
  27. 27.
    Mondal C, Halder AK, Adhikari N, Jha T (2014) Structural findings of cinnolines as anti-schizophrenic PDE10A inhibitors through comparative chemometric modeling. Mol Divers 18(3):655–671PubMedGoogle Scholar
  28. 28.
    Wu Q, Gao Q, Guo H, Li D, Wang J, Gao W, Han C, Li Y, Yang L (2013) Inhibition mechanism exploration of quinoline derivatives as PDE10A inhibitors by in silico analysis. Mol BioSyst 9(3):386–397PubMedGoogle Scholar
  29. 29.
    Goodarzi M, Saeys W, Deeb O, Pieters S, Vander Heyden Y (2013) Particle swarm optimization and genetic algorithm as feature selection techniques for the QSAR modeling of imidazo [1, 5-a] pyrido [3, 2-e] pyrazines, inhibitors of phosphodiesterase 10 a. Chem Biol Drug Des 82(6):685–696PubMedGoogle Scholar
  30. 30.
    Gholami Rostami E, Fatemi MH (2018) Comparative molecular field analysis and hologram quantitative structure activity relationship studies of pyrimidine series as potent phosphodiesterase 10A inhibitors. J Chin Chem Soc-Taip 65(11):1293–1306Google Scholar
  31. 31.
    Shipe WD, Sharik SS, Barrow JC, McGaughey GB, Theberge CR, Uslaner JM, Yan Y, Renger JJ, Smith SM, Coleman PJ (2015) Discovery and optimization of a series of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors through fragment screening, structure-based design, and parallel synthesis. J Med Chem 58(19):7888–7894PubMedGoogle Scholar
  32. 32.
    Release H (2002) 7.5 for windows, molecular modeling system, Hypercube. Inc http://www hyper comGoogle Scholar
  33. 33.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791PubMedPubMedCentralGoogle Scholar
  34. 34.
    Morris GM, Huey R, Olson AJ (2008) Using autodock for ligand-receptor docking. Curr Protoc Bioinformatics 24(1):8.14. 11–18.14. 40Google Scholar
  35. 35.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662Google Scholar
  36. 36.
    Durrant JD, McCammon JA (2011) BINANA: a novel algorithm for ligand-binding characterization. J Mol Graph Model 29(6):888–893PubMedPubMedCentralGoogle Scholar
  37. 37.
    Talete, srl., Dragon (software for molecular descriptor calculation) version 3.0. 〈
  38. 38.
    Katritzky AR, Lobanov VS, Karelson M, Murugan R, Grendze MP, Toomey J (1996) Comprehensive descriptors for structural and statistical analysis. 1: correlations between structure and physical properties of substituted pyridines. Rev Roum Chim 41(11–12):851–867Google Scholar
  39. 39.
    Roy K, Roy PP (2009) Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques. Eur J Med Chem 44(7):2913–2922PubMedGoogle Scholar
  40. 40.
    Liu P, Long W (2009) Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 10(5):1978–1998PubMedPubMedCentralGoogle Scholar
  41. 41.
    Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design, vol 20. Pws Pub, BostonGoogle Scholar
  42. 42.
    Zupan J, Gasteiger J (1993) Neural networks for chemists: an introduction. John Wiley & Sons, Inc. VCH, Weinheim. J ChemomGoogle Scholar
  43. 43.
    Fatemi M (2003) Quantitative structure–property relationship studies of migration index in microemulsion electrokinetic chromatography using artificial neural networks. J Chromatogr A 1002(1–2):221–229PubMedGoogle Scholar
  44. 44.
    Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300Google Scholar
  45. 45.
    Suykens J, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific Publishing, SingaporeGoogle Scholar
  46. 46.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New YorkGoogle Scholar
  47. 47.
    Maldonado AG, Doucet J, Petitjean M, Fan B-T (2006) Molecular similarity and diversity in chemoinformatics: from theory to applications. Mol Divers 10(1):39–79PubMedGoogle Scholar
  48. 48.
    Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905PubMedPubMedCentralGoogle Scholar
  49. 49.
    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 Syst 152:18–33Google Scholar
  50. 50.
    Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27(3):302–313Google Scholar
  51. 51.
    Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276Google Scholar
  52. 52.
    Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN (2013) Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data. J Comput Chem 34(12):1071–1082PubMedGoogle Scholar
  53. 53.
    Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107(1):194–205Google Scholar
  54. 54.
    Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29(6–7):476–488PubMedGoogle Scholar
  55. 55.
    Roy K, Ambure P, Aher RB (2017) How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? Chemom Intell Lab Syst 162:44–54Google Scholar
  56. 56.
    Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22(1):69–77Google Scholar
  57. 57.
    Pratim Roy P, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14(5):1660–1701PubMedPubMedCentralGoogle Scholar
  58. 58.
    Mitra I, Saha A, Roy K (2009) Quantitative structure–activity relationship modeling of antioxidant activities of hydroxybenzalacetones using quantum chemical, physicochemical and spatial descriptors. Chem Biol Drug Des 73(5):526–536PubMedGoogle Scholar
  59. 59.
    Todeschini R, Consonni V (2008) Handbook of molecular descriptors, vol 11. John Wiley & Sons, New YorkGoogle Scholar
  60. 60.
    Papa E, Dearden J, Gramatica P (2007) Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors. Chemosphere 67(2):351–358PubMedGoogle Scholar
  61. 61.
    Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96(3):1027–1044PubMedPubMedCentralGoogle Scholar
  62. 62.
    Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set descriptor space: a review. ATLA-Altern Lab Anim 33(5):445Google Scholar
  63. 63.
    Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environ Health Persp 111(10):1361–1375Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Chemometrics, Faculty of ChemistryUniversity of MazandaranBabolsarIran

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