1. 厚生労働省「薬事工業生産動態統計年報」、日本製薬工業協会 DATABOOK2019
2. Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 2006, 5, 993-996.
3. Hopkins, A. L.; Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 2002, 1, 727- 730.
4. Molecular Operating Environment (MOE); Chemical Computing Group Inc.: Montreal, QC, Canada, https://www.chemcomp.com.
5. Edelsbrunner, H.; Facello, M.; Fu R.; Liang, J. Proceedings of the 28th Hawaii Int. Conf. on Systems Science 1995, 256-264.
6. Maestro; Schrödinger, LLC: New York, NY, USA, http://www.schrodinger.com.
7. Halgren, T. Identifying and characterizing binding sites and assessing druggability J. Chem. Inf. Model. 2009, 49, 377–389.
8. Halgren, T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 2007, 69, 146–148.
9. Bowers, K. J.; Chow, E.; Xu, H.; Dror, R. O.; Eastwood, M. P.; Gregersen, B. A.; Klepeis, J. L. ;Kolossvary, I.; Moraes, M. A.; Sacerdoti, F. D.; Salmon, J. K.; Shan, Y.; Shaw, D. E. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, 2006, November 11-17.
10. Altschul, S. F.; Gish, W.; Miller, W.; Myers, E. W.; Lipman,D. J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403-410.
11. Ress, D. C.; Congreve, M.; Murray, C. W.; Carr, R. Fragment-based lead discovery. Nat. Rev. Drug Disc. 2004, 3, 660-672.
12. Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discovery Today 2004, 9, 430-431.
13. Leeson, P. D.; Springthorpe, B. Nat. Rev. The influence of drug-like concepts on decisionmaking in medicinal chemistry. Drug Disc. 2007, 6, 881-890.
14. Jorgensen, W. L. The many roles of computation in drug discovery. Science 2004, 303, 1813−1818.
15. Lyne, P. D. Structure-based virtual screening: an overview. Drug Discovery Today 2002, 7, 1047−1055.
16. Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligandbased virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model. 2010, 50, 205-216.
17. Kirchmair, J.; Ristic, S.; Eder, K.; Markt, P.; Wolber, G.; Laggner, C.; Langer, T. Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches. J. Chem. Inf. Model. 2007, 47, 2182−2196.
18. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235−242.
19. Chothia, C.; Lesk A. M. The relation between the divergence of sequence and structure in proteins. EMBO J. 1986, 5, 823-826.
20. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shaw, D. E.; Shelley, M.; Perry, J. K.; Francis, P.; Shenkin, P. S. Glide: A new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. J. Med. Chem. 2004, 47, 1739–1749.
21. GOLD; The Cambridge Crystallographic Data Centre: Cambridge, UK, https://www.ccdc.cam.ac.uk.
22. Jones, G.; Willett, P.; Glen, R. C.; Leach A. R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 1997, 267, 727-748.
23. AutoDock; The Center for Computational Structural Biology: California, USA, https://ccsb.scripps.edu.
24. Morris, G. M.; Goodsell, D. S.; Halliday, R.S.; Huey, R.; Hart, W. E.; Belew, R. K; Olson, A. J. Automated docking using a lamarckian genetic algorithm and and empirical binding free energy function. J. Computational Chemistry 2009, 16, 2785-2791.
25. FlexX; BioSolveIT: Sankt Augustin, Germany, https://www.biosolveit.de.
26. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996, 261, 470–489.
27. Tanimoto, T. T. An elementary mathematical theory of classification and prediction. Internal IBM Technical Report 1957.
28. Lo, Y. C.; Rensi, S. E.; Torng, W.;Altman, R. B. Machine learning in chemoinformatics and drug discovery. Drug Discovery Today 2018, 23, 1538-1546.
29. Pipeline Pilot; BIOVIA: San Diego, CA, USA, https://www.3dsbiovia.com.
30. Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 2010, 50, 742-754.
31. Daylight; Chemical Information Systems, Inc.: Laguna Niguel, CA, USA, https://www.daylight.com.
32. Durant, J.; Leland, B. A.; Henry, D. R.; Nourse, J. G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 2002, 42, 1273–1280.
33. ROCS; OpenEye Scientific Software: Santa Fe, NM, USA, http://www.eyesopen.com.
34. Hawkins, P. C. D.; Skillman, A. G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem. 2007, 50, 74-82.
35. Abel, R.; Young, T.; Farid, R.; Berne, B. J.; Friesner, R. A. Role of the active-site solvent in the thermodynamics of Factor Xa ligand binding. J. Am. Chem. Soc. 2008, 130, 2817-2831.
36. Young, T.; Abel, R.; Kim, B.; Berne, B. J.; Friesner, R. A. Motifs for molecular recognition exploiting hydrophobic enclosure in protein−ligand binding. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 808-813.
37. Schneider, P.; Walters, W. P.; Plowright, A. T.; Sieroka, N.; Listgarten, J.; Goodnow Jr., R. A.; Fisher, J.; Jansen, J. M.; Duca, J. S.; Rush, T. S.; Zentgraf, M.; Hill, J. E.; Krutoholow, E.; Kohler, M.; Blaney, J.; Funatsu, K.; Luebkemann, C.; Schneider, G. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 2020, 19, 353−364.
38. Fogolari, F.; Brigo, A.; Molinari, H. Protocol for MM/PBSA molecular dynamics simulations of proteins. Biophys. J. 2003, 85, 159-166.
39. Wager, T. T.; Hou, X.; Verhoset, P.; Villalobos, A. Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem. Neurosci. 2010, 1, 420-434.
40. Fisher, N. D.; Hollenberg, N. K. Renin inhibition: what are the therapeutic opportunities? J. Am. Soc. Nephrol. 2005, 16, 592-599.
41. MacGregor, G. A.; Markandu, N. D.; Roulston, J. E.; Jones, J. C.; Morton, J. J. Maintenance of blood pressure by the renin–angiotensin system in normal man. Nature 1981, 291, 329- 331.
42. White, W. B. Update on the drug treatment of hypertension in patients with cardiovascular disease. Am. J. Med. 2005, 118, 695−705.
43. Skeggs, L. C.; Kahn, J. R.; Lentz, K.; Shumway, N. P. The preparation, purification, and amino acid sequence of a polypeptide renin substrate. J. Exp. Med. 1957, 106, 439-453.
44. Raia, J. J.; Barone, J. A.; Byerly, W. G.;Lacy, C. R. Angiotensin-converting enzyme inhibitors: a comparative review. Ann. Pharmacother. 1990, 24, 506-525.
45. Chrisa Dimou, C.; Antza, C.; Akrivos, E.; Doundoulakis, I.; Stabouli, S.; Haidich, A. B.; Kotsis, V. A systematic review and network meta-analysis of the comparative efficacy of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers in hypertension. J. Hum. Hypertens. 2019, 33, 188–201.
46. Hollenberg, N. K. Direct renin inhibition and the kidney. Nat. Rev. Nephrol. 2010, 6, 49- 55.
47. Mende, C. W. Application of direct renin inhibition to chronic kidney disease. Cardiovasc. Drugs Ther. 2010, 24, 139-149.
48. Leung, D.; Abbenante, G.; Fairlie, D. P. Protease inhibitors: current status and future prospects. J. Med. Chem. 2000, 43, 305–341.
49. Zhou, A.; Carrell, R. W.; Murphy, M. P.; Wei, Z.; Yan, Y.; Stanley, P. L.; Stein, P. E.; Pipkin, F. B.; Read, R. J.; A redox switch in angiotensinogen modulates angiotensin release. Nature 2010, 468, 108.
50. Webb, D. J.; Cumming, A. M.; Leckie, B. J.; Lever, A. F.; Morton, J. J.; Robertson, J. I.; Szelke, M.; Donovan, B. Reduction of blood pressure in man with H-142, a potent new renin inhibitor. Lancet 1983, 2, 1486-1487.
51. Wood, J. M.; Criscione, L.; de Gasparo, M.; Bühlmayer, P.; Rüeger, H.; Stanton, J. L.; Jupp, R. A.; Kay, J. CGP38560: orally active, low-molecular-weight renin inhibitor with high potency and specificity. J Cardiovasc Pharmacol. 1989, 14, 221-226.
52. Richter, W. F.; Whitby, B. R.; Chou, R. C. Distribution of remikiren, a potent orally active inhibitor of human renin, in laboratory animals. Xenobiotica. 1996, 26, 243–254.
53. Maibaum, J.; Stutz, S.; Göschke, R.; Rigollier, P.; Yamaguchi, Y.; Cumin, F.; Rahuel, J.; Baum, H. P.; Cohen, N. C.; Schnell, C. R.; Fuhrer, W.; Gruetter, M. G.; Schilling, W.; Wood, J. M. Structural modification of the P2‘position of 2,7-dialkyl-substituted 5(S)-amino-4(S)- hydroxy-8-phenyl-octanecarboxamides: the discovery of aliskiren, a potent nonpeptide human renin inhibitor active after once daily dosing in marmosets. J. Med. Chem. 2007, 50, 4832-4844.
54. Oh, B. H.; Mitchell, J.; Herron, J. R.; Chung, J.; Khan, M.; Keefe, D. L. Aliskiren, an oral renin inhibitor, provides dose-dependent efficacy and sustained 24-hour blood pressure control in patients with hypertension. J. Am. Coll. Cardiol. 2007, 49, 1157-1163.
55. Vaidyanathan, S.; Jarugula, V.; Dieterich, H. A.; Howard, D.; Dole, W. P. Clinical pharmacokinetics and pharmacodynamics of aliskiren. Clin. Pharm. 2008, 47, 515-531.
56. Bonanni, L.; Vestra, M. D. Oral renin inhibitors in clinical practice: a perspective review. Ther Adv Chronic Dis. 2012, 3, 73-181.
57. Wells, L.; Vosseller, K.; Hart, G. W. Glycosylation of nucleocytoplasmic proteins: signal transduction and O-GlcNAc. Science 2001, 291, 2376-2378.
58. Yuzwa, S. A.; Shan, X.; Macauley, M. S.; Clark, T.;Skorobogatko, Y.; Vosseller, K.; Vocadlo, D. J. Increasing O-GlcNAc slows neurodegeneration and stabilizes tau against aggregation. Nat. Chem. Biol. 2012, 8, 393−399.
59. Zhu, Y.; Shan, X.; Yuzwa, S. A.; Vocadlo, D. J. The emerging link between O-GlcNAc and Alzheimer disease. J. Biol. Chem. 2014, 289, 34472−34481.
60. Gao, Y.; Wells, L.; Comer, F. I.; Parker, G. J.; Hart, G. W. Dynamic O-Glycosylation of nuclear and cytosolic proteins: cloning and characterization of a neural, cytosolic β-Nacetylglucosaminidase from human brain. J. Biol. Chem. 2001, 276, 9838-9845.
61. Wells, L.; Gao, Y.; Mahoney, J. A.; Vosseller, K.; Chen, C.; Rosen, A.; Hart, G. W. Dynamic O-Glycosylation of nuclear and cytosolic proteins: further characterization of the nucleocytoplasmic β-N-acetylglucosaminidase, O-GlcNAcase. J. Biol. Chem. 2002, 277, 1755−1761.
62. Gong, C.-X.; Liu, F.; Iqbal, K. O-GlcNAcylation: a regulator of tau pathology and neurodegeneration. Alzheimer's Dementia 2016, 12, 1078−1089.
63. Yuzwa, S. A.; Vocadlo, D. J. O-GlcNAc and neurodegeneration: biochemical mechanisms and potential roles in alzheimer's disease and beyond. Chem. Soc. Rev. 2014, 43, 6839-6858.
64. Liu, F.; Iqbal, K.; G.-Iqbal, I.; Hart, G. W.; Gong, C. O-GlcNAcylation regulates phosphorylation of tau: a mechanism involved in Alzheimer’s disease. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 10804-10809.
65. Liu, F.; Shi, J.; Tanimukai, H.; Gu, J.; Gu, J.; G.-Iqbal, I.; Iqbal, K.; Gong, C. X. Reduced O-GlcNAcylation links lower brain glucose metabolism and tau pathology in Alzheimer’s disease. Brain 2009, 132, 1820−1832.
66. Yuzwa, S. A.; Macauley, M. S.; Heinonen, J. E.; Shan, X.; Dennis, R. J.; He, Y.; Whitworth, G. E.; Stubbs, K. A.; Mceachern, E. J.; Davies, G. J.; Vocadlo, D. J. A potent mechanisminspired O-GlcNAcase inhibitor that blocks phosphorylation of tau in vivo. Nat. Chem. Biol. 2008, 4, 483−490.
67. Macauley, M. S.; Bubb, A.; Martinez-Fleites, C.; Davies, G. J.; Vocadlo, D. J. Elevation of global O-GlcNAc levels in 3T3-L1 adipocytes by selective inhibition of O-GlcNAcase does not induce insulin resistance. J. Biol. Chem. 2008, 283, 34687−34695.
68. Macauley, M. S.; He, Y.; Gloster, T. M.; Stubbs, K. A.; Davies, G. J.; Vocadlo, D. J. Inhibition of O-GlcNAcase using a potent and cell-permeable inhibitor does not induce insulin resistance in 3T3-L1 adipocytes. Chem. Biol. 2010, 17, 937−948.
69. Bergeron-Brlek, M.; Goodwin-Tindall, J.; Cekic, N.; Roth, C.; Zandberg, W. F.; Shan, X.; Varghese, V.; Chan, S.; Davies, G. J.; Vocadlo, D. J.; Britton, R. A convenient approach to stereoisomeric iminocyclitols: generation of potent brain-permeable OGA inhibitors. Angew. Chem., Int. Ed. 2015, 54, 15429−15433.
70. Dorfmueller, H. C.; Borodkin, V. S.; Schimpl, M.; Van Aalten, D. M. F. GlcNAcstatins are nanomolar inhibitors of human O-GlcNAcase inducing cellular hyper-O-GlcNAcylation. Biochem. J. 2009, 420, 221−227.
71. Balcewich, M. D.; Stubbs, K. A.; He, Y.; James, T. W.; Davies, G. J.; Vocadlo, D. J.; Mark, B. L. Insight into a strategy for attenuating AmpC- mediated beta-lactam resistance: structural basis for selective inhibition of the glycoside hydrolase Nagz. Protein Sci. 2009, 18, 1541−1551.
72. Selnick, H. G.; Hess, J. F.; Tang, C.; Liu, K.; Schachter, J. B.; Ballard, J. E.; Marcus, J.; Klein, D. J.; Wang, X.; Pearson, M.; Savage, M. J.; Kaul, R.; Li, T.-S.; Vocadlo, D. J.; Zhou, Y.; Zhu, Y.; Mu, C.; Wang, Y.; Wei, Z.; Bai, C.; Duffy, J. L.; McEachern, E. J. Discovery of MK8719, a Potent O-GlcNAcase inhibitor as a potential treatment for tauopathies. J. Med. Chem. 2019, 62, 10062−10097.
73. Corminboeuf, O.; Bezencon, O.; Grisostomi, C.; Remen, L.; Richard-Bildstein; S.; Bur, D.; Prade, L.; Hess, P.; Strickner, P.; Fischli, W.; Steiner, B.; Treiber, A. Design and optimization of new piperidines as renin inhibitors. Bioorg. Med. Chem. Lett. 2010, 20, 6286-6290.
74. Rahuel, J.; Rasetti, V.; Maibaum, J.; Rueger, H.; Goschke, R.; Cohen, N. C.; Stutz, S.; Cumin, F.; Fuhrer, W.; Wood, J. M.; Grutter, M.G. Structure-based drug design: the discovery of novel nonpeptide orally active inhibitors of human renin. Chem. Biol. 2000, 7, 493-504.
75. Rahuel, J.; Priestle, J. P.; Grutter, M. G. The crystal structures of recombinant glycosylated human renin alone and in complex with a transition state analog inhibitor. J. Struct. Biol. 1991, 107, 227-236.
76. Ehara, T.; Irie, O.; Kosaka, T.; Kanazawa, T.; Breitenstein, W.; Grosche, P.; Ostermann, N.; Suzuki, M.; Kawakami, S.; Konishi, K.; Hitomi, Y.; Toyao, A.; Gunji, H.; Cumin, F.; Schiering, N.; Wagner, T.; Rigel, D. F.; Webb, R. L.; Maibaum, J.; Yokokawa, F. Structurebased design of substituted piperidines as a new class of highly efficacious oral direct Renin inhibitors. ACS Med. Chem. Lett. 2014, 5, 787-792.
77. Webb, R. L.; Schiering, N.; Sedrani, R.; Maibaum, J. Direct renin inhibitors as a new therapy for hypertension. J. Med. Chem. 2010, 53, 7490-7520.
78. Ostermann, N.; Ruedisser, S.; Ehrhardt, C.; Breitenstein, W.; Marzinzik, A.; Jacoby, E.; Vangrevelinghe, E.; Ottl, J.; Hartwieg, J. C. D.; Cumin, F.; Haissiepen, U.; Trappe, T.; Sedrani, R.; Geisse, S.; Gerhartz, B.; Richert, P.; Francotte, E.; Wagner, T.; Krömer, M.; Kosaka, T.; Webb, R. L.; Rigel, D. F.; Maibaum, J.; Baeschlin, D. J. A novel class of oral direct renin inhibitors: highly potent 3,5-disubstituted piperidines bearing a tricyclic P3– P1 pharmacophore. J. Med. Chem. 2013, 56, 2196-2206.
79. Viera, E.; Binggeli, A.; Breu, V.; Bur, D.; Fischli, W.; Güller, R.; Hirth, G.; Märki, H. P.; Müller, M.; Oefner, C.; Scalone, M.; Stadler, H.; Wilhelm, M.; Wostl, W. Substituted piperidines - highly potent renin inhibitors due to induced fit adaptation of the active site. Bioorg. Med. Chem. Lett. 1999, 9, 1397-1402.
80. Sarver, R. W.; Peevers, J.; Cody, W. L.; Ciske, F. L.; Dyer, J.; Emerson, S. D.; Hagadorn, J. C.; Holsworth, D. D.; Jalaie, M. Binding thermodynamics of substituted diaminopyrimidine renin inhibitors. Anal. Biochem. 2007, 360, 30-40.
81. OMEGA; OpenEye Scientific Software: Santa Fe, NM, USA, http://www.eyesopen.com.
82. Imaeda, Y; Tokuhara, H.; Fukase, Y.; Kanagawa, R.; Kajimoto, Y.; Kusumoto, K.; Kondo, M.; Snell, G.; Behnke, C. A.; Kuroita, T. Discovery of TAK-272: a novel, potent, and orally active renin inhibitor. ACS Med. Chem. Lett. 2016, 7, 933–938.
83. Barone, J. A.; Peters, E.; Tieckelmann, H. 2-Trifluoromethylpyrimidines. J. Org. Chem. 1959, 24, 198-200.
84. Lappin, G. R. Cyclization of 2-aminopyridine derivatives. I. Substituted ethyl 2- pyridylaminomethylenemalonates. J. Am. Chem. Soc. 1948, 70, 3348-3350.
85. Dong, L.; Shen, S.; Chen, W.; Xu, D.; Yang, Q.; Lu, H.; Zhang, J. Discovery of novel inhibitors targeting human O-GlcNAcase: docking-based virtual screening, biological evaluation, structural modification, and molecular dynamics simulation. J. Chem. Inf. Model. 2019, 59, 4374−4382.
86. Baell, J. B.;Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 2010, 53, 7, 2719–2740.
87. LigPrep; Schrödinger, LLC: New York, NY, USA, http://www.schrodinger.com. 88. Hirozane, Y.; Toyofuku, M.; Yogo, T.; Tanaka, Y.; Sameshima, T.; Miyahisa, I.; Yoshikawa, M. Structure-based rational design of staurosporine-based fluorescent probe with broadranging kinase affinity for kinase panel application. Bioorg. Med. Chem. Lett. 2019, 29, 126641−126643.
89. Hohenstein, E. G.; Sherrill, C. D. Effects of heteroatoms on aromatic π-π interactions: benzene pyridine and pyridine dimer. J. Phys. Chem. A 2009, 113, 878−886.
90. Structural Genomics Consortium (SGC): Toronto, ON, Canada, https://www.thesgc.org.
91. Samani, N. J.; Cumin, F.; Kelly, M.; Wood, J. M. Expression of components of the RAS during prolonged blockade at different levels in primates. Am. J. Physiol. 1994, 267, E612- 619.
92. Hosfield, D.; Palan, J.; Hilgers, M.; Scheibe, D.; McRee, D. E.; Stevens, R. C. A fully integrated protein crystallization platform for small-molecule drug discovery. J. Struct. Biol. 2003, 142, 207-217.
93. Otwinowski, Z.; Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 1997, 276, 307-326.
94. Vagin, A.; Teplyakov, A. MOLREP: an automated program for molecular replacement. J. Appl. Cryst. 1997, 30, 1022-1025.
95. McRee, D. E. XtalView/Xfit--a versatile program for manipulating atomic coordinates and electron density . J. Struct. Biol. 1999, 125, 156-165.
96. Emsley, P.; Cowtan, K. Coot: model-building tools for molecular graphics. Acta Cryst. D 2004, 60, 2126-2132.
97. Winn, M. D.; Murshudov, G. N.; Papiz, M. Z. Macromolecular TLS refinement in REFMAC at moderate resolutions. Methods Enzymol. 2003, 374, 300-321.
98. The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019, 47, D506−D515.
99. Winn, M. D.; Ballard, C. C.; Cowtan, K. D.; Dodson, E. J.; Emsley, P.; Evans, P. R.; Keegan, R. M.; Krissinel, E. B.; Leslie, A. G. W.; McCoy, A.; McNicholas, S. J.; Murshudov, G. N.; Pannu, N. S.; Potterton, E. A.; Powell, H. R.; Read, R. J.; Vagin, A.; Wilson, K. S. Overview of the CCP4 suite and current developments. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2011, 67, 235−242.
100. Emsley, P.; Lohkamp, B.; Scott, W. G.; Cowtan, K. Features and development of Coot. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2010, 66, 486−501.