SYNOPSIS: SYNthesize and OPti mize System in Silico

Journal of Medicinal Chemistry

Volumn 46, Issue 13, 2003, Pages 2765-2773

  Vinkers, H Maarten ^a   De Jonge, Marc R ^a   Daeyaert, Frederik F D ^a   Heeres, Jan ^a   Koymans, Lucien M H ^a   Van Lenthe, Joop H ^b   Lewi, Paul J ^a   Timmerman, Henk ^c   Van Aken, Koen ^a   Janssen, Paul A J ^a  

^a JANSSEN RESEARCH FOUNDATION   (Belgium)

^b UTRECHT UNIVERSITY   (Netherlands)

^c VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

VIRUS ENZYME; ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; DRUG; RNA DIRECTED DNA POLYMERASE INHIBITOR;

ARTICLE; COMPUTER PROGRAM; DATA BASE; DIPOLE; DRUG SCREENING; ENZYME STRUCTURE; HUMAN IMMUNODEFICIENCY VIRUS; SYNTHESIS; CHEMICAL STRUCTURE; CHEMISTRY; COMBINATORIAL CHEMISTRY; DRUG DESIGN; FACTUAL DATABASE; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

ANTI-HIV AGENTS; COMBINATORIAL CHEMISTRY TECHNIQUES; DATABASES, FACTUAL; DRUG DESIGN; MOLECULAR STRUCTURE; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REVERSE TRANSCRIPTASE INHIBITORS; SOFTWARE;

EID: 0037777697     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm030809x     Document Type: Article

References (53)

1. Labinger, J. A. Tuning into better catalysts. Science 1995, 269, 1833-1833.
2. Thomas, J. M. Design, synthesis, and in situ characterization of new solid catalysts. Angew. Chem., Int. Ed. 1999, 38, 3588-3628.
3. Venkatasubramanian, V.; Chan, K.; Caruthers, J. M. Computer-aided molecular design using genetic algorithms. Comput. Chem. Eng. 1994, 18, 833-844.
4. Ulmer, II, C. W.; Smith, D. A.; Sumpter, B. G.; Noid, D. I. Computational neural networks and the rational design of polymeric materials: The next generation polycarbonates. Comput. Theor. Polymer Sci. 1998, 8, 311-321.
5. Dahiyat, B. I.; Sarisky, C. A.; Mayo, S. L. De novo protein design: Towards fully automated sequence selection. J. Mol. Biol. 1997, 273, 789-796.
6. Hellinga, H. W. Rational protein design: Combining theory and experiment. Proc. Natl. Acad. Sci. U.S.A. 1997, 94, 10015- 10017.
7. Street, A. G.; Mayo, S. L. Computational protein design. Structure 1999, 7, 105-109.
8. Joseph-McCarthy, D. Computational approaches to structure-based ligand design. Pharmacol. Ther. 1999, 84, 179-191.
9. Kuntz, I. D. Structure-based strategies for drug design and discovery. Science 1992, 257, 1078-1082.
10. Lunney, E. A. Structure-based drug design begins a new era. Med. Chem. Res. 1998, 8, 352-361.
11. Murcko, M. A.; Caron, P. R.; Charifson, P. S. Structure-based drug design. Annu. Rep. Med. Chem. 1999, 34, 297-306.
12. Shoichet, B. K.; Bussiere, D. E. The role of macromolecular crystallography and structure for drug discovery: Advances and caveats. Curr. Opin. Drug Discov. Devel. 2000, 3, 408-422.
13. Bohacek, R. S.; McMartin, C.; Guida, W. C. The art and practice of structure-based drug design: A molecular modeling perspective. Med. Res. Rev. 1996, 16, 3-50.
14. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3-25.
15. Bohacek, R. S.; McMartin, C. Multiple highly diverse structures complementary to enzyme binding sites: Results of extensive application of a de novo design method incorporating combinatorial growth. J. Am. Chem. Soc. 1994, 116, 5560-5571.
16. DeWitte, R. S.; Shakhnovich, E. I. SMOG: De novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J. Am. Chem. Soc. 1996, 118, 11733-11744.
17. Gillet, V. J.; Johnson, A. P.; Mata, P.; Sike, S. Automated structure design in 3D. Tetrahedron Comput. Methodol. 1990, 3, 681-696.
18. Nishibata, Y.; Itai, A. Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 1991, 47, 8985-8990.
19. Rotstein, S. H.; Murcko, M. A. GenStar: A method for de novo drug design. J. Comput.-Aided Mol. Des. 1993, 7, 23-43.
20. Rotstein, S. H.; Murcko, M. A. GroupBuild: A fragment-based method for de novo drug design. J. Med. Chem. 1993, 36, 1700- 1710.
21. Moon, J. B.; Howe, W. J. Computer design of bioactive molecules: A method for receptor-based de novo ligand design. Proteins: Struct., Funct., Genet. 1991, 11, 314-328.
22. Böhm, H. J. The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput.-Aided Mol. Des. 1992, 6, 61-78.
23. Miranker, A.; Karplus, M. An automated method for dynamic ligand design. Proteins: Struct., Funct., Genet. 1995, 23, 472- 490.
24. Tschinke, V.; Cohen, N. C. The NEWLEAD program: A new method for the design of candidate structures from pharmacophoric hypotheses. J. Med. Chem. 1993, 36, 3863-3870.
25. Gehlhaar, D. K.; Moerder, K. E.; Zichi, D.; Sherman, C. J.; Ogden, R. C.; Freer, S. T. De novo design of enzyme inhibitors by Monte Carlo ligand generation. J. Med. Chem. 1995, 38, 466-472.
26. Lewis, R. A.; Dean, P. M. Automated site-directed drug design: The formation of molecular templates in primary structure generation. Proc. R. Soc. London, Ser. B 1989, 236, 141-162.
27. Pearlman, D. A.; Murcko, M. A. CONCEPTS: New dynamic algorithm for de novo drug suggestion. J. Comput. Chem. 1993, 14, 1184-1193.
28. Pearlman, D. A.; Murcko, M. A. CONCERTS: Dynamic collection of fragments as an approach to de novo ligand design. J. Med. Chem. 1996, 39, 1651-1663.
29. Gillet, V. J.; Myatt, G.; Zsoldos, Z.; Johnson, A. P. SPROUT, HIPPO and CAESA: Tools for de novo structure generation and estimation of synthetic accessibility. Perspect. Drug Discov. Des. 1995, 3, 34-50.
30. Holloway, M. K. A priori prediction of ligand affinity by energy minimization. Perspect. Drug Discov. Des. 1998, 9/10/11, 63-84.
31. Schneider, G. M. L.; Stahl, M.; Schneider, P. De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J. Comput.-Aided Mol. Des. 2000, 14, 487-494.
32. Available Chemicals Directory; Molecular Design Limited Information Systems, San Leandro, CA, 1999.
33. Stone, A. J. Distributed multipole analysis; or how to describe a molecular charge distribution. Chem. Phys. Lett. 1981, 83, 233-239.
34. Guest, M. F.; van Lenthe, J. H.; Kendrick, J.; Schoffel, K.; Sherwood, P. GAMESS-UK; CCLRC Daresbury Laboratory: Daresbury, UK, 1999.
35. Metropolis, N.; Rosenbluth, A.; Rosenbluth, M.; Teller, A.; Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 1953, 21, 1087-1092.
36. Kirkpatrick, S.; Gelatt, C. D. J.; Vecchi, M. P. Optimization by simulated annealing. Science 1983, 220, 671-680.
37. Holland, J. Adaptation in natural and artificial systems; MIT Press: Cambridge, MA, 1992.
38. Clark, D. E.; Westhead, D. R. Evolutionary algorithms in computer-aided molecular design. J. Comput.-Aided Mol. Des. 1996, 10, 337-358.
39. de Jonge, M. R. CMDFF, in preparation.
40. Luenberger, D. G. Linear and nonlinear programming; Addison-Wesley: Reading, MA, 1984.
41. Nash, S. G.; Sofer, A. Linear and nonlinear programming; McGraw-Hill: New York, 1996.
42. Ponder, J. W. TINKER: Software tools for molecular design; Washington University, School of Medicine: St. Louis, MO, 1999.
43. Nash, S. G. Newton-type minimization via the Lanczos method. SIAM J. Numer. Anal. 1984, 21, 770-778.
44. http://www.netlib.org.
45. Halgren, T. A. MMFF VI. MMFF94s option for energy minimization studies. J. Comput. Chem. 1999, 20, 720-729.
46. Dauber-Osguthorpe, P.; Roberts, V. A.; Osguthorpe, D. J.; Wolff, J.; Genest, M.; Hagler, A. T. Structure and energetics of ligand binding to proteins: E. coli dihydrofolate reductase-trimethoprim, a drug-receptor system. Proteins: Struct., Funct., Genet. 1988, 4, 31-47.
47. Dewar, M. J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P. AM1: A new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 1985, 107, 3902-3909.
48. Stewart, J. J. P. MOPAC; Quantum Chemistry Program Exchange (QCPE #455); Indiana University: Bloomington, IN, 1990.
49. Pauwels, R.; Balzarini, J.; Baba, M.; Snoeck, R.; Schols, D.; Herdewijn, P.; Desmyter, J.; De Clercq, E. Rapid and automated tetrazolium-based colorimetric assay for the detection of anti-HIV compounds. J. Virol. Methods 1988, 20, 309-321.
50. Teague, S. J.; Davis, A. M.; Leeson, P. D.; Oprea, T. The design of leadlike combinatorial libraries. Angew. Chem., Int. Ed. 1999, 38, 3743-3748.
51. Böhm, H. J.; Boehringer, M.; Bur, D.; Gmuender, H.; Huber, W.; Klaus, W.; Kostrewa, D.; Kuehne, H.; Luebbers, T.; Meunier-Keller, N.; Mueller, F. Novel inhibitors of DNA Gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J. Med. Chem. 2000, 43, 2664-2674.
52. Hann, M. A.; Leach, A. R.; Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 2001, 41, 856-864.
53. Miller, J. Aromatic nucleophilic substitution; Elsevier: Amsterdam: The Netherlands, 1968.