A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinaselike chemical space

Journal of Cheminformatics

Volumn 4, Issue 2, 2012, Pages

  Singh, Narender ^a   Sun, Hongmao ^b   Chaudhury, Sidhartha ^a   AbdulHameed, Mohamed Diwan M ^a   Wallqvist, Anders ^a   Tawa, Gregory ^a  

^a U S ARMY MEDICAL RESEARCH AND MATERIEL COMMAND   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84862662736     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-4-4     Document Type: Article

References (74)

1. Dobson CM: Chemical space and biology. Nature 2004, 432:824-828. (Pubitemid 40037137)
2. Lipinski C, Hopkins A: Navigating chemical space for biology and medicine. Nature 2004, 432:855-861. (Pubitemid 40037141)
3. Bohacek RS, McMartin C, Guida WC: The art and practice of structurebased drug design: a molecular modeling perspective. Med Res Rev 1996, 16:3-50. (Pubitemid 26037075)
4. Ertl P: Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comput Sci 2003, 43:374-380.
5. Ogata K, Isomura T, Yamashita H, Kubodera H: A Quantitative Approach to the Estimation of Chemical Space from a Given Geometry by the Combination of Atomic Species. QSAR & Combinatorial Science 2007, 26:596-607. (Pubitemid 46938089)
6. Chemical Abstracts Service. [http://www.cas.org].
7. Chemical Structure Lookup Service. [http://cactus.nci.nih.gov/cgi-bin/ lookup/search].
8. PubChem. [http://pubchem.ncbi.nlm.nih.gov].
9. Chemspider. [http://www.chemspider.com].
10. ZINC. [http://zinc.docking.org].
11. Hann MM, Oprea TI: Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol 2004, 8:255-263. (Pubitemid 38759400)
12. Koch MA, Schuffenhauer A, Scheck M, Wetzel S, Casaulta M, Odermatt A, Ertl P, Waldmann H: Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc Natl Acad Sci USA 2005, 102:17272-17277. (Pubitemid 41740856)
13. Chen G, Zheng S, Luo X, Shen J, Zhu W, Liu H, Gui C, Zhang J, Zheng M, Puah CM, et al: Focused combinatorial library design based on structural diversity, druglikeness and binding affinity score. J Comb Chem 2005, 7:398-406. (Pubitemid 40774851)
14. Mok NY, Brenk R: Mining the ChEMBL Database: An Efficient Chemoinformatics Workflow for Assembling an Ion Channel-Focused Screening Library. J Chem Inf Model 2011, 51:2449-2454.
15. Deng Z, Chuaqui C, Singh J: Knowledge-based design of target-focused libraries using protein-ligand interaction constraints. J Med Chem 2006, 49:490-500. (Pubitemid 43157483)
16. Schnur D, Beno BR, Good A, Tebben A: Approaches to target class combinatorial library design. Methods Mol Biol 2004, 275:355-378
17. Schreyer SK, Parker CN, Maggiora GM: Data shaving: a focused screening approach. J Chem Inf Comput Sci 2004, 44:470-479.
18. Di L, Kerns EH: Profiling drug-like properties in discovery research. Curr Opin Chem Biol 2003, 7402-408.
19. Horio K, Muta H, Goto J, Hirayama N: A simple method to improve the odds in finding 'lead-like' compounds from chemical libraries. Chem Pharm Bull (Tokyo) 2007, 55:980-984. (Pubitemid 47026529)
20. Khanna V, Ranganathan S: Physiochemical property space distribution among human metabolites, drugs and toxins. BMC Bioinformatics 2009, 10(Suppl 15):S10.
21. Raevsky OA, Trepalin SV, Trepalina HP, Gerasimenko VA, Raevskaja OE: SLIPPER-2001 - software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity. J Chem Inf Comput Sci 2002, 42:540-549. (Pubitemid 35355260)
22. van de Waterbeemd H, Gifford E: ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2003, 2:192-204. (Pubitemid 37361664)
23. Ajay A, Walters WP, Murcko MA: Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? J Med Chem 1998, 41:3314-3324. (Pubitemid 28406538)
24. Egan WJ, Walters WP, Murcko MA: Guiding molecules towards druglikeness. Curr Opin Drug Discov Devel 2002, 5:540-549.
25. Lajiness MS, Vieth M, Erickson J: Molecular properties that influence oral drug-like behavior. Curr Opin Drug Discov Devel 2004, 7:470-477. (Pubitemid 44383553)
26. Lipinski CA: Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 2000, 44:235-249. (Pubitemid 32239479)
27. Muegge I: Selection criteria for drug-like compounds. Med Res Rev 2003, 23:302-321 (Pubitemid 36457537)
28. Muegge I, Heald SL, Brittelli D: Simple selection criteria for drug-like chemical matter. J Med Chem 2001, 44:1841-1846 (Pubitemid 32884510)
29. Oprea TI, Gottfries J: Chemography: the art of navigating in chemical space. J Comb Chem 2001, 3:157-166. (Pubitemid 33614499)
30. Proudfoot JR: Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg Med Chem Lett 2002, 12:1647-1650. (Pubitemid 34569263)
31. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD: Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002, 45:2615-2623. (Pubitemid 34595232)
32. Walters WP, Murcko MA: Recognizing molecules with drug-like properties. Curr Opin Chem Biol 1999, 3:384-387. (Pubitemid 29376099)
33. Walters WP, Murcko MA: Prediction of 'drug-likeness'. Adv Drug Deliv Rev 2002, 54:255-271
34. Wang J, Ramnarayan K: Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds. J Comb Chem 1999, 1:524-533
35. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews 1997, 23:3-25 (Pubitemid 27046991)
36. Oprea TI: Current trends in lead discovery: are we looking for the appropriate properties? Mol Divers 2002, 5:199-208
37. Oprea TI, Davis AM, Teague SJ, Leeson PD: Is there a difference between leads and drugs? A historical perspective. J Chem Inf Comput Sci 2001, 41:1308-1315.
38. Balakin KV, Tkachenko SE, Lang SA, Okun I, Ivashchenko AA, Savchuk NP: Property-based design of GPCR-targeted library. J Chem Inf Comput Sci 2002, 42:1332-1342. (Pubitemid 35468320)
39. Zhang J, Yang PL, Gray NS: Targeting cancer with small molecule kinase inhibitors. Nat Rev Cancer 2009, 9:28-39.
40. Aronov AM, McClain B, Moody CS, Murcko MA: Kinase-likeness and kinaseprivileged fragments: toward virtual polypharmacology. J Med Chem 2008, 51:1214-1222. (Pubitemid 351480383)
41. Brenk R, Schipani A, James D, Krasowski A, Gilbert IH, Frearson J, Wyatt PG: Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 2008, 3435-444.
42. Keri G, Szekelyhidi Z, Banhegyi P, Varga Z, Hegymegi-Barakonyi B, Szantai-Kis C, Hafenbradl D, Klebl B, Muller G, Ullrich A, et al: Drug discovery in the kinase inhibitory field using the Nested Chemical Library technology. Assay Drug Dev Technol 2005, 3:543-551. (Pubitemid 41824372)
43. Lowrie JF, Delisle RK, Hobbs DW, Diller DJ: The different strategies for designing GPCR and kinase targeted libraries. Comb Chem High Throughput Screen 2004, 7:495-510. (Pubitemid 39115322)
44. Xi H, Lunney EA: The design, annotation, and application of a kinasetargeted library. Methods Mol Biol 2011, 685:279-291.
45. Zhang C, Bollag G: Scaffold-based design of kinase inhibitors for cancer therapy. Curr Opin Genet Dev 2010, 20:79-86.
46. Deanda F, Stewart EL, Reno MJ, Drewry DH: Kinase-targeted library design through the application of the PharmPrint methodology. J Chem Inf Model 2008, 48:2395-2403.
47. Decornez H, Gulyas-Forro A, Papp A, Szabo M, Sarmay G, Hajdu I, Cseh S, Dorman G, Kitchen DB: Design, selection, and evaluation of a general kinase-focused library. ChemMedChem 2009, 4:1273-1278.
48. Sun D, Chuaqui C, Deng Z, Bowes S, Chin D, Singh J, Cullen P, Hankins G, Lee WC, Donnelly J, et al: A kinase-focused compound collection: compilation and screening strategy. Chem Biol Drug Des 2006, 67:385-394. (Pubitemid 44086975)
49. Cohen P: Protein kinases-the major drug targets of the twenty-first century? Nat Rev Drug Discov 2002, 1:309-315. (Pubitemid 37361447)
50. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S: The protein kinase complement of the human genome. Science 2002, 298:1912-1934. (Pubitemid 35425239)
51. Bossemeyer D: Protein kinases-structure and function. FEBS Lett 1995, 369:57-61.
52. Milletti F, Vulpetti A: Predicting polypharmacology by binding site similarity: from kinases to the protein universe. J Chem Inf Model 2010, 50:1418-1431.
53. ChEMBL. [http://www.ebi.ac.uk/chembldb].
54. National Cancer Institute. [http://dtp. nci.nih.gov/webdata.html].
55. Austin CP, Brady LS, Insel TR, Collins FS: NIH Molecular Libraries Initiative. Science 2004, 306:1138-1139. (Pubitemid 39488980)
56. Ohno K, Nagahara Y, Tsunoyama K, Orita M: Are there differences between launched drugs, clinical candidates, and commercially available compounds? J Chem Inf Model 2010, 50:815-821.
57. Irwin JJ, Shoichet BK: ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005, 45:177-182. (Pubitemid 40736970)
58. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009, 37:W623-633.
59. World Drug Index. Dervent Information: London, UK; 1999.
60. The Molecular Operating Environment Version 2010.10, Chemical Computing Group Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Canada H3A 2R7, 2010. [http://www.chemcomp. com].
61. Pipeline Pilot, version 8.0. Accelrys San Diego, California, USA; 2010.
62. Wager TT, Chandrasekaran RY, Hou XT, Verhoest PR, Villalobos A, Will Y: Defining Desirable Central Nervous System Drug Space through the Alignment of Molecular Properties, in Vitro ADME, and Safety Attributes. ACS Chemical Neuroscience 2010, 1:420-434.
63. Oashi T, Ringer AL, Raman EP, Mackerell AD: Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness. J Chem Inf Model 2011, 51:148-158.
64. LigPrep, version 2.4.107. Schrodinger, LLC, New York, NY; 2010.
65. Epik, version 2.1.209. Schrodinger, LLC, New York, NY; 2010.
66. Glide, version 5.6.109. Schrodinger, LLC, New York, NY; 2010.
67. ChemNavigator. [https://www.chemnavigator.com/cnc/products/iRL.asp].
68. O'Farrell AM, Abrams TJ, Yuen HA, Ngai TJ, Louie SG, Yee KW, Wong LM, Hong W, Lee LB, Town A, et al: SU11248 is a novel FLT3 tyrosine kinase inhibitor with potent activity in vitro and in vivo. Blood 2003, 101:3597-3605. (Pubitemid 36857948)
69. Rogers D, Hahn M: Extended-connectivity fingerprints. J Chem Inf Model 2010, 50:742-754.
70. Bland JM: An Introduction To Medical Statistics. 2 edition. Oxford: Oxford University Press; 1995.
71. Corder GW, Foreman DI: Nonparametric statistics for non-statisticians: a stepby-step approach New York: Wiley; 2009.
72. King AJ, Patrick DR, Batorsky RS, Ho ML, Do HT, Zhang SY, Kumar R, Rusnak DW, Takle AK, Wilson DM, et al: Demonstration of a genetic therapeutic index for tumors expressing oncogenic BRAF by the kinase inhibitor SB-590885. Cancer Res 2006, 66:11100-11105. (Pubitemid 46009933)
73. Wu JP, Wang J, Abeywardane A, Andersen D, Emmanuel M, Gautschi E, Goldberg DR, Kashem MA, Lukas S, Mao W, et al: The discovery of carboline analogs as potent MAPKAP-K2 inhibitors. Bioorg Med Chem Lett 2007, 17:4664-4669. (Pubitemid 47058930)
74. Anderson M, Beattie JF, Breault GA, Breed J, Byth KF, Culshaw JD, Ellston RP, Green S, Minshull CA, Norman RA, et al: Imidazo[1, 2-a]pyridines: a potent and selective class of cyclin-dependent kinase inhibitors identified through structure-based hybridisation. Bioorg Med Chem Lett 2003, 13:3021-3026. (Pubitemid 36993629)