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Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies

  • Saw Simeon
  • Nathjanan Jongkon
  • Warot Chotpatiwetchkul
  • M. Paul GleesonEmail author
Article
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Abstract

Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure–activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r2 ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

Keywords

EGFR kinase Quinazoline Pairwise interactions Quantum mechanics 3D-QSAR 

Notes

Acknowledgements

M.P.G. would like to acknowledge the financial support provided by the Thailand Research Fund (RSA6180073) and King Mongkut’s Institute of Technology Ladkrabang. S.S. is grateful for financial support from the National Research University (NRU) for supporting his Ph.D. Studies. We would like to thank the Large Scale Research Laboratory of the National Electronics and Computer Technology (NECTEC) for SYBYLX2.0 software.

Supplementary material

10822_2019_221_MOESM1_ESM.docx (710 kb)
Supplementary Material 1 (DOCX 710 kb)
10822_2019_221_MOESM2_ESM.xlsx (156 kb)
Supplementary Material 2 (XLSX 156 kb)

References

  1. 1.
    Blume-Jensen P, Hunter T (2001) Oncogenic kinase signalling. Nature 411:355–365CrossRefGoogle Scholar
  2. 2.
    Lemmon MA, Schlessinger J (2010) Cell signaling by receptor tyrosine kinases. Cell 141:1117–1134CrossRefGoogle Scholar
  3. 3.
    Hubbard SR, Miller WT (2007) Receptor tyrosine kinases: mechanisms of activation and signaling. Curr Opin Cell Biol 19:117–123CrossRefGoogle Scholar
  4. 4.
    Weinmann H, Metternich R (2005) Drug discovery process for kinase inhibitors. Chem Biol Chem 6:455–459CrossRefGoogle Scholar
  5. 5.
    Manning G, Whyte D, Martinez R, Hunter T, Sudarsanam S (2002) The protein kinase complement of the human genome. Science 298:1912–1934CrossRefGoogle Scholar
  6. 6.
    Arora A, Scholar EM (2005) Role of tyrosine kinase inhibitors in cancer therapy. J Pharmacol Exp Ther 315:971–979CrossRefGoogle Scholar
  7. 7.
    Wu P, Nielsen TE, Clausen MH (2016) Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discov Today 21:5–10CrossRefGoogle Scholar
  8. 8.
    Ferguson FM, Gray NS (2018) Kinase inhibitors: the road ahead. Nat Rev Drug Discov 17:353CrossRefGoogle Scholar
  9. 9.
    Herbst RS (2004) Review of epidermal growth factor receptor biology. Int J Radiat Oncol 59:S21–S26CrossRefGoogle Scholar
  10. 10.
    Zaczek A, Brandt B, Bielawski KP (2005) The diverse signaling network of EGFR, HER2, HER3 and HER4 tyrosine kinase receptors and the consequences for therapeutic approaches. Histol Histopathol 20:1005–1015Google Scholar
  11. 11.
    Raymond E, Faivre S, Armand JP (2000) Epidermal growth factor receptor tyrosine kinase as a target for anticancer therapy. Drugs 60:15–23CrossRefGoogle Scholar
  12. 12.
    Khan I, Ibrar A, Abbas N, Saeed A (2014) Recent advances in the structural library of functionalized quinazoline and quinazolinone scaffolds: synthetic approaches and multifarious applications. Eur J Med Chem 76:193–244CrossRefGoogle Scholar
  13. 13.
    Selvam TP, Kumar PV (2011) Quinazoline marketed drugs. Res Pharm 1(1):1–21Google Scholar
  14. 14.
    Liao JJ-L (2007) Molecular recognition of protein kinase binding pockets for design of potent and selective kinase inhibitors. J Med Chem 50:409–424CrossRefGoogle Scholar
  15. 15.
    Noble MEM, Endicott JA, Johnson LN (2004) Protein kinase inhibitors: insights into drug design from structure. Science 303:1800–1805CrossRefGoogle Scholar
  16. 16.
    Pettersen E, Goddard T, Huang C, Couch G, Greenblatt D, Meng E, Ferrin T (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  17. 17.
    Michel J (2014) Current and emerging opportunities for molecular simulations in structure-based drug design. Phys Chem Chem Phys 16:4465–4477CrossRefGoogle Scholar
  18. 18.
    Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395CrossRefGoogle Scholar
  19. 19.
    Kaur M, Silakari O (2017) Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3). J Biomol Struct Dyn 35:3043–3060CrossRefGoogle Scholar
  20. 20.
    Zhang W, Qiu K-X, Yu F, Xie X-G, Zhang S-Q, Chen Y-J, Xie H-D (2017) Virtual screening of B-Raf kinase inhibitors: a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy calculation studies. Comput Biol Chem 70:186–190CrossRefGoogle Scholar
  21. 21.
    Yang Y, Qin J, Liu H, Yao X (2011) Molecular dynamics simulation, free energy calculation and structure-based 3D-QSAR studies of B-RAF kinase inhibitors. J Chem Inf Model 51:680–692CrossRefGoogle Scholar
  22. 22.
    Martinez A, Alonso M, Castro A, Dorronsoro I, Gelpí JL, Luque FJ, Pérez C, Moreno FJ (2005) SAR and 3D-QSAR studies on thiadiazolidinone derivatives: exploration of structural requirements for glycogen synthase kinase 3 inhibitors. J Med Chem 48:7103–7112CrossRefGoogle Scholar
  23. 23.
    Dessalew N, Patel DS, Bharatam PV (2007) 3D-QSAR and molecular docking studies on pyrazolopyrimidine derivatives as glycogen synthase kinase-3β inhibitors. J Mol Graph Model 25:885–895CrossRefGoogle Scholar
  24. 24.
    Kamath S, Buolamwini JK (2003) Receptor-guided alignment-based comparative 3D-QSAR studies of benzylidene malonitrile tyrphostins as EGFR and HER-2 kinase inhibitors. J Med Chem 46:4657–4668CrossRefGoogle Scholar
  25. 25.
    Assefa H, Kamath S, Buolamwini JK (2003) 3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors. J Comput Aided Mol Des 17:475–493CrossRefGoogle Scholar
  26. 26.
    Nandi S, Bagchi MC (2011) Activity prediction of some nontested anticancer compounds using GA-based PLS regression models. Chem Biol Drug Des 78:587–595CrossRefGoogle Scholar
  27. 27.
    Chen G, Luo X, Zhu W, Luo C, Liu H, Puah CM, Chen K, Jiang H (2004) Elucidating inhibitory models of the inhibitors of epidermal growth factor receptor by docking and 3D-QSAR. Bioorg Med Chem 12:2409–2417CrossRefGoogle Scholar
  28. 28.
    Thaimattam R, Daga PR, Banerjee R, Iqbal J (2005) 3D-QSAR studies on c-Src kinase inhibitors and docking analyses of a potent dual kinase inhibitor of c-Src and c-Abl kinases. Bioorg Med Chem 13:4704–4712CrossRefGoogle Scholar
  29. 29.
    Singh S, Dessalew N, Bharatam P (2006) 3D-QSAR CoMFA study on indenopyrazole derivatives as cyclin dependent kinase 4 (CDK4) and cyclin dependent kinase 2 (CDK2) inhibitors. Eur J Med Chem 41:1310–1319CrossRefGoogle Scholar
  30. 30.
    Leach AR (1996) Molecular modelling: principles and applications. Longman, HarlowGoogle Scholar
  31. 31.
    Martin YC (1998) 3D QSAR: current state, scope, and limitations. Perspect Drug Discov Des 12:3–23CrossRefGoogle Scholar
  32. 32.
    Alexander D, Tropsha A, Winkler DA (2015) Beware of R 2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55:1316–1322CrossRefGoogle Scholar
  33. 33.
    Damm-Ganamet KL, Smith RD, Dunbar JB, Stuckey JA, Carlson HA (2013) CSAR Benchmark Exercise 2011–2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model 53:1853–1870CrossRefGoogle Scholar
  34. 34.
    Kubinyi H (1997) QSAR and 3D QSAR in drug design, part 1: methodology. Drug Discov Today 2:457–467CrossRefGoogle Scholar
  35. 35.
    Clark M, Cramer RD III, Jones DM, Patterson DE, Simeroth PE (1990) Comparative molecular field analysis (CoMFA). 2. Toward its use with 3D-structural databases. Tetrahedron Comput Methodol 3:47–59CrossRefGoogle Scholar
  36. 36.
    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  37. 37.
    Labute P (2000) A widely applicable set of descriptors. J Mol Graph Model 18:464–477CrossRefGoogle Scholar
  38. 38.
    Pastor M, Cruciani G, McLay I, Pickett S, Clementi S (2000) GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 43:3233–3243CrossRefGoogle Scholar
  39. 39.
    Kier LB (1989) An index of flexibility from molecular shape descriptors. Prog Clin Biol Res 291:105–109Google Scholar
  40. 40.
    Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein–ligand interactions. Docking and scoring: successes and gaps. J Med Chem 49:5851–5855CrossRefGoogle Scholar
  41. 41.
    Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931CrossRefGoogle Scholar
  42. 42.
    Yilmazer ND, Korth M (2013) Comparison of molecular mechanics, semi-empirical quantum mechanical, and density functional theory methods for scoring protein–ligand interactions. J Phys Chem B 117:8075–8084CrossRefGoogle Scholar
  43. 43.
    Mobley DL, Gilson MK (2017) Predicting binding free energies: frontiers and benchmarks. Annu Rev Biophys 46:531–558CrossRefGoogle Scholar
  44. 44.
    Stjernschantz E, Oostenbrink C (2010) Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 98:2682–2691CrossRefGoogle Scholar
  45. 45.
    Gleeson MP, Gleeson D (2009) QM/MM calculations in drug discovery: a useful method for studying binding phenomena? J Chem Inf Model 49(3):670–677CrossRefGoogle Scholar
  46. 46.
    Zhou T, Huang DZ, Caflisch A (2010) Quantum mechanical methods for drug design. Curr Top Med Chem 10:33–45CrossRefGoogle Scholar
  47. 47.
    Jing YQ, Han KL (2010) Quantum mechanical effect in protein–ligand interaction. Expert Opin Drug Discov 5:33–49CrossRefGoogle Scholar
  48. 48.
    Raha K, Peters MB, Wang B, Yu N, Wollacott AM, Westerhoff LM, Merz KM (2007) The role of quantum mechanics in structure-based drug design. Drug Discov Today 12:725–731CrossRefGoogle Scholar
  49. 49.
    Peters MB, Raha K, Merz KM (2006) Quantum mechanics in structure-based drug design. Curr Opin Drug Discov Dev 9:370–379Google Scholar
  50. 50.
    Shaw KE, Woods CJ, Mulholland AJ, Abraham DJ (2003) QM and QM/MM approaches to evaluating binding affinities. In: Burger’s medicinal chemistry and drug discovery. Wiley, HobokenGoogle Scholar
  51. 51.
    Ash J, Fourches D (2017) Characterizing the chemical space of ERK2 kinase inhibitors using descriptors computed from molecular dynamics trajectories. J Chem Inf Model 57:1286–1299CrossRefGoogle Scholar
  52. 52.
    Merz KM, Peters MB (2006) Semiempirical comparative binding energy analysis (SE-COMBINE) of a series of trypsin inhibitors. J Chem Theor Comput 2:383–399CrossRefGoogle Scholar
  53. 53.
    Raha K, van der Vaart AJ, Riley KE, Peters MB, Westerhoff LM, Kim H, Merz KM (2005) Pairwise decomposition of residue interaction energies using semiempirical quantum mechanical methods in studies of protein–ligand interaction. J Am Chem Soc 127:6583–6594CrossRefGoogle Scholar
  54. 54.
    Dixon S, Merz KM Jr, Lauri G, Ianni JC (2005) QMQSAR: utilization of a semiempirical probe potential in a field-based QSAR method. J Comput Chem 26:23–34CrossRefGoogle Scholar
  55. 55.
    Zhang X, Gibbs AC, Reynolds CH, Peters MB, Westerhoff LM (2010) Quantum mechanical pairwise decomposition analysis of protein kinase b inhibitors: validating a new tool for guiding drug design. J Chem Inf Model 50:651–661CrossRefGoogle Scholar
  56. 56.
    Kubinyi H (2008) Comparative molecular field analysis. (CoMFA). In: Handbook of chemoinformatics. Wiley-VCH Verlag GmbH, Weinheim, pp 1555–1574Google Scholar
  57. 57.
    Stamos J, Sliwkowski MX, Eigenbrot C (2002) Structure of the epidermal growth factor receptor kinase domain alone and in complex with a 4-ANILINOQUINAZOLINE Inhibitor. J Biol Chem 277:46265–46272CrossRefGoogle Scholar
  58. 58.
    Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, Abola EE (1998) Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr Sect D 54:1078–1084CrossRefGoogle Scholar
  59. 59.
    Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28:45–48CrossRefGoogle Scholar
  60. 60.
    Stanton CL, Houk KN (2008) Benchmarking pKa prediction methods for residues in proteins. J Chem Theor Comput 4:951–966CrossRefGoogle Scholar
  61. 61.
    Olsson MHM, Søndergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theor Comput 7:525–537CrossRefGoogle Scholar
  62. 62.
    Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25CrossRefGoogle Scholar
  63. 63.
    Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725CrossRefGoogle Scholar
  64. 64.
    Thompson EJ, DePaul AJ, Patel SS, Sorin EJ (2010) Evaluating molecular mechanical potentials for helical peptides and proteins. PLoS ONE 5:e10056CrossRefGoogle Scholar
  65. 65.
    Jongkon N, Gleeson D, Gleeson MP (2018) Elucidation of the catalytic mechanism of 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase using QM/MM calculations. Org Biomol Chem 16:6239–6249CrossRefGoogle Scholar
  66. 66.
    Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25:247–260CrossRefGoogle Scholar
  67. 67.
    Wang J, Wolf R, Caldwell J, Kollman P, Case D (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174CrossRefGoogle Scholar
  68. 68.
    Price DJ, Brooks CL III (2004) A modified TIP3P water potential for simulation with Ewald summation. J Chem Phys 121:10096–10103CrossRefGoogle Scholar
  69. 69.
    Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105:9954–9960CrossRefGoogle Scholar
  70. 70.
    Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190CrossRefGoogle Scholar
  71. 71.
    Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718CrossRefGoogle Scholar
  72. 72.
    Garofalo A, Farce A, Ravez S, Lemoine A, Six P, Chavatte P, Goossens L, Depreux P (2012) Synthesis and structure–activity relationships of (aryloxy) quinazoline ureas as novel, potent, and selective vascular endothelial growth factor receptor-2 inhibitors. J Med Chem 55:1189–1204CrossRefGoogle Scholar
  73. 73.
    Domarkas J, Dudouit F, Williams C, Qiyu Q, Banerjee R, Brahimi F, Jean-Claude BJ (2006) The combi-targeting concept: synthesis of stable nitrosoureas designed to inhibit the epidermal growth factor receptor (EGFR). J Med Chem 49:3544–3552CrossRefGoogle Scholar
  74. 74.
    Wright SW, Carlo AA, Carty MD, Danley DE, Hageman DL, Karam GA, Levy CB, Mansour MN, Mathiowetz AM, McClure LD (2002) Anilinoquinazoline inhibitors of fructose 1,6-bisphosphatase bind at a novel allosteric site: synthesis, in vitro characterization, and X-ray crystallography. J Med Chem 45:3865–3877CrossRefGoogle Scholar
  75. 75.
    Bridges AJ, Zhou H, Cody DR, Rewcastle GW, McMichael A, Showalter HH, Fry DW, Kraker AJ, Denny WA (1996) Tyrosine kinase inhibitors. 8. An unusually steep structure–activity relationship for analogues of 4-(3-bromoanilino)-6,7-dimethoxyquinazoline (PD 153035), a potent inhibitor of the epidermal growth factor receptor. J Med Chem 39:267–276CrossRefGoogle Scholar
  76. 76.
    Rewcastle GW, Denny WA, Bridges AJ, Zhou H, Cody DR, McMichael A, Fry DW (1995) Tyrosine kinase inhibitors. 5. Synthesis and structure-activity relationships for 4-[(phenylmethyl) amino]-and 4-(phenylamino) quinazolines as potent adenosine 5'-triphosphate binding site inhibitors of the tyrosine kinase domain of the epidermal growth factor receptor. J Med Chem 38:3482–3487CrossRefGoogle Scholar
  77. 77.
    de Castro Barbosa ML, Lima LM, Tesch R, Sant'Anna CMR, Totzke F, Kubbutat MH, Schächtele C, Laufer SA, Barreiro EJ (2014) Novel 2-chloro-4-anilino-quinazoline derivatives as EGFR and VEGFR-2 dual inhibitors. Eur J Med Chem 71:1–14CrossRefGoogle Scholar
  78. 78.
    Lü S, Zheng W, Ji L, Luo Q, Hao X, Li X, Wang F (2013) Synthesis, characterization, screening and docking analysis of 4-anilinoquinazoline derivatives as tyrosine kinase inhibitors. Eur J Med Chem 61:84–94CrossRefGoogle Scholar
  79. 79.
    Ravez S, Arsenlis S, Barczyk A, Dupont A, Frédérick R, Hesse S, Kirsch G, Depreux P, Goossens L (2015) Synthesis and biological evaluation of di-aryl urea derivatives as c-Kit inhibitors. Bioorg Med Chem 23:7340–7347CrossRefGoogle Scholar
  80. 80.
    Garofalo A, Goossens L, Six P, Lemoine A, Ravez S, Farce A, Depreux P (2011) Impact of aryloxy-linked quinazolines: a novel series of selective VEGFR-2 receptor tyrosine kinase inhibitors. Bioorg Med Chem Lett 21:2106–2112CrossRefGoogle Scholar
  81. 81.
    Ballard P, Bradbury RH, Harris CS, Hennequin LF, Hickinson M, Johnson PD, Kettle JG, Klinowska T, Leach AG, Morgentin R (2006) Inhibitors of epidermal growth factor receptor tyrosine kinase: novel C-5 substituted anilinoquinazolines designed to target the ribose pocket. Bioorg Med Chem Lett 16:1633–1637CrossRefGoogle Scholar
  82. 82.
    Rachid Z, Brahimi F, Domarkas J, Jean-Claude BJ (2005) Synthesis of half-mustard combi-molecules with fluorescence properties: correlation with EGFR status. Bioorg Med Chem Lett 15:1135–1138CrossRefGoogle Scholar
  83. 83.
    Myers MR, Setzer NN, Spada AP, Zulli AL, Hsu C-YJ, Zilberstein A, Johnson SE, Hook LE, Jacoski MV (1997) The preparation and sar of 4-(anilino), 4-(phenoxy), and 4-(thiophenoxy)-quinazolines: Inhibitors of p56 lck and EGF-R tyrosine kinase activity. Bioorg Med Chem Lett 7:417–420CrossRefGoogle Scholar
  84. 84.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B (2011) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107CrossRefGoogle Scholar
  85. 85.
    ChemAxon Standardizer (2012) Standardizer Version 5.12. ChemAxon, BudapestGoogle Scholar
  86. 86.
    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein–ligand docking using GOLD. Proteins 52:609–623CrossRefGoogle Scholar
  87. 87.
    Gleeson MP, Gleeson D (2009) QM/MM as a tool in fragment based drug discovery: a cross-docking, rescoring study of kinase inhibitors. J Chem Inf Model 49:1437–1448CrossRefGoogle Scholar
  88. 88.
    Frisch M, Trucks G, Schlegel H, Scuseria G, Robb M, Cheeseman J, Scalmani G, Barone V, Mennucci B, Petersson G (2010) Gaussian Inc., Wallingford CTGoogle Scholar
  89. 89.
    Gleeson D, Tehan B, Gleeson MP, Limtrakul J (2012) Evaluating the enthalpic contribution to ligand binding using QM calculations: effect of methodology on geometries and interaction energies. Org Biomol Chem 10:7053–7061CrossRefGoogle Scholar
  90. 90.
    ChemAxon JChem. www.chemaxon.com
  91. 91.
    Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474CrossRefGoogle Scholar
  92. 92.
    Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931CrossRefGoogle Scholar
  93. 93.
    Lo Y-C, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23:1538–1546CrossRefGoogle Scholar
  94. 94.
    Jenkins JL (2012) Large-scale QSAR in target prediction and phenotypic HTS assessment. Mol Inf 31:508–514CrossRefGoogle Scholar
  95. 95.
    Gleeson MP, Hersey A, Montanari D, Overington J (2011) Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat Rev Drug Discov 10:197–208CrossRefGoogle Scholar
  96. 96.
    Hann MM, Keserü GM (2012) Finding the sweet spot: the role of nature and nurture in medicinal chemistry. Nat Rev Drug Discov 11:355–365CrossRefGoogle Scholar
  97. 97.
    Hann MM (2011) Molecular obesity, potency and other addictions in drug discovery. Med Chem Commun 2:349–355CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Interdisciplinary Graduate Program in Bioscience, Faculty of ScienceKasetsart UniversityBangkokThailand
  2. 2.Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced StudiesKasetsart UniversityBangkokThailand
  3. 3.Department of Social and Applied Science, College of Industrial TechnologyKing Mongkut’s University of Technology North BangkokBangkokThailand
  4. 4.Department of Chemistry, Faculty of ScienceKing Mongkut’s Institute of Technology LadkrabangBangkokThailand
  5. 5.Department of Chemistry, Faculty of ScienceKasetsart UniversityBangkokThailand
  6. 6.Department of Biomedical Engineering, Faculty of EngineeringKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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