De Novo Molecular Design

Evolutionary Algorithms in Molecular Design

Volumn 8, Issue , 2008, Pages 49-69

  Gillet, Valerie J ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Chemical industries; Compounds; Evolution strategy; Pharmaceutical; Root mean square

Indexed keywords

DRUG PRODUCTS;

COMPOUNDS; EVOLUTION STRATEGIES; MOLECULAR DESIGN; ROOT MEAN SQUARE;

CHEMICAL INDUSTRY;

EID: 84956737652     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9783527613168.ch4     Document Type: Chapter

References (61)

1. R. S. Bohacek, C. McMartin, W. C. Guida, The Art and Practice of Structure-Based Drug Design: A Molecular Modeling Perspective, Med. Res. Rev. 1996,16, 3-50.
2. J. H. Holland, Adaption in Natural and Artificial Systems, MIT Press, Cambridge, MA, 1992.
3. D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, Piscataway, NJ, 1995.
4. D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.
5. H.-P. Schwefel, Numerical Optimization of Computer Models, Wiley, Chichester, 1981.
6. L. J. Fogel, A. J. Owens, M. J. Walsh, Artificial Intelligence through Simulated Evolution, Wiley, New York, 1996.
7. D. E. Clark, Some Current Trends in Evolutionary Algorithm Research Exemplified by Applica-tions in Computer-Aided Molecular Design, MATCH 1998, 38, 85-98.
8. D. E. Clark, Evolutionary Algorithms in Rational Drug Design: A Review of Current Applica-tions and a Look to the Future, in A. L. Parrill, M. R. Reddy (Eds.), Rational Drug Design: Novel Methodology and Practical Applications, ACS Symposium Series Vol. 719, American Chemical Society, Washington DC, 1999, pp. 255-270.
9. J. Devillers (Ed.), Genetic Algorithms in Molecular Modelling, Academic Press Limited, New York, 1996.
10. G. Jones, Genetic and Evolutionary Algorithms, in P. v. R. Schleyer, N. L. Allinger, T. Clark, J. Gasteiger, P. A. Kollman, H. F. Schaefer III, P. R. Schreiner (Eds.), Encyclopedia of Computational Chemistry, John Wiley & Sons, Chichester, UK, 1998, Volume 2, pp. 1127-1136.
11. A. L. Parrill, Evolutionary and Genetic Methods in Drug Design, Drug Discovery Today 1996, 1, 514-521.
12. D. E. Clark, D. R. Westhead, Evolutionary Algorithms in Computer-Aided Molecular Design, J. Comput.-Aided. Mol. Des. 1996,10, 337-358.
13. R. Judson, Genetic Algorithms and Their Use in Chemistry, in K. B. Lipkowitz, D. B. Boyd (Eds.), Reviews in Computational Chemistry, Wiley-VCH, New York, 1997, Volume 10, pp. 1-73.
14. P. J. Goodford, A Computational-Procedure for Determining Energetically Favorable Binding-Sites on Biologically Important Macromolecules, J. Med. Chem. 1985, 28, 849-857.
15. R. C. Wade, P. J. Goodford, Further Development of Hydrogen-Bonding Functions For Use in Determining Energetically Favorable Binding-Sites on Molecules of Known Structure. 2. Ligand Probe Groups with the Ability to Form More Than Two Hydrogen-Bonds, J. Med. Chem. 1993, 36, 148-156.
16. D. J. Danziger, P. M. Dean, Automated Site Directed Drug Design - The Prediction and Obser-vation of Ligand Point Positions at Hydrogen-Bonding Regions on Protein Surfaces, Proc. R. Soc. London 1989, B236, 115-124.
17. H.-J. Bohm, Site-Directed Structure Generation by Fragment-Joining, Perspect. Drug Discovery Des. 1995, 3, 21-33.
18. V. J. Gillet, G. Myatt, Z. Zsoldos, A. P. Johnson, SPROUT, HIPPO and CAESA: Tools for De Novo Structure Generation and Estimation of Synthetic Accessibility, Perspect. Drug Discovery Des. 1995, 3, 34-50
19. L. B. Kier, Molecular Orbital Theory in Drug Research, Academic Press, New York, 1971.
20. G. M. Downs, P. Willett, Similarity Searching in Databases of Chemical Structures, in K. B. Lip-kowitz, D. B. Boyd (Eds.), Reviews in Computational Chemistry, Wiley-VCH, New York, 1995, Volume 7, pp. 1-66.
21. [21] V. J. Gillet, A. P. Johnson, Structure Generation for De Novo Design, in Y. C. Martin, P. Willett (Eds.), Designing Bioactive Molecules, American Chemical Society, Washington DC, 1998, pp. 149-174.
22. M. A. Murcko, Recent Advances in Ligand Design Methods, in K. B. Lipkowitz, D. B. Boyd (Eds.), Reviews in Computational Chemistry, Wiley-VCH, New York, 1997, Volume 11, pp. 1-65.
23. D. E. Clark, C. W. Murray, J. Li, Current Issues in De Novo Molecular Design, in K. B. Lipko-witz, D. B. Boyd (Eds.), Reviews in Computational Chemistry, Wiley-VCH, New York, 1997, Volume 11, pp. 67-125.
24. P. Mata, V. J. Gillet, A. P. Johnson, J. Lampreia, G. Myatt, S. Sike, A. L. Stebbings, SPROUT: 3-D Structure Generation Using Templates, J. Chem. Inf. Comput. Sci. 1995, 35, 479-193.
25. J. B. Moon, W J. Howe, Computer Design of Bioactive Molecules - A Method for Receptor-Based De Novo Ligand Design, Proteins: Struct. Fund. Genet. 1991, 11, 314-328.
26. Y. Nishibata, A. Itai, Confirmation of Usefulness of a Structure Construction Program Based on Three-Dimensional Receptor Structure For Rational Lead Generation, J. Med. Chem. 1993, 36, 2921-2928.
27. R. C. Glen, A. W. R. Payne, A Genetic Algorithm for the Automated Generation of Molecules within Constraints, I. Comput.-Aided. Mot Des. 1995, 9, 181-202.
28. J. M. Blaney, J. S. Dixon, D. J. Weininger, Evolution of Molecules to Fit a Binding Site of Known Structure. Paper presented at the Molecular Graphics Society Meeting on Binding Sites: Characterising and Satisfying Steric and Chemical Restraints, York, U.K., March 1993. (Abstracts of this and other papers presented at this meeting are available from Prof. R. E. Hubbard, Department of Chemistry, University of York, York, YOl 5DD, United Kingdom. E-mail: rod@yorvic.york.ac.uk).
29. D. J. Weininger, SMILES a Chemical Language and Information System. 1. Introduction to Methodology and Encoding Rules, /. Chem. Inf. Comput. Sci. 1988, 28, 31-36.
30. The Medchem database is maintained by The MedChem Project and BioByte Corp., 201 W. Fourth St. Claremont, CA, USA.
31. J. M. Blaney, J. S. Dixon, Receptor Modeling By Distance Geometry, Ann. Rep. Med. Chem. 1991, 26, 281-285.
32. D. Weininger, Method and Apparatus for Designing Molecules with Desired Properties by Evol-ving Successive Populations, US Patent 5434796, 1995.
33. LeapFrog is available from TRIPOS Inc., 1699 South Hanley Road, Suite 303, St. Louis, MO 63144.
34. H. Kubinyi (Ed.), 3-D QSAR in Drug Design, ESCOM, Leiden, 1993.
35. J. M. Jansen, K. F. Koehler, M. H. Hedberg, A. M. Johansson, U. Hacksell, G. Nordvall, J. P. Synder, Molecular Design Using the Minireceptor Concept, /. Chem. Inf. Comput. Sci. 1997, 37, 812-818.
36. A. Vedani, P. Zbinden, J. P. Snyder, P. A. Greenidge, Pseudoreceptor Modeling: The Construc-tion of Three-Dimensional Receptor Surrogates, /. Am. Chem. Soc. 1995,117, 4987-1994.
37. D. R. Westhead, D. E. Clark, D. Frenkel, J. Li, C. W. Murray, B. Robson, B. Waszkowycz, PRO_LIGAND: An Approach to De Novo Molecular Design. 3. A Genetic Algorithm for Structure Refinement, /. Comput.-Aided. Mol. Des. 1995, 9, 139-145.
38. M. Sullivan, Taking Drug Discovery to New Heights, Today's Chemist at Work 1999, January, 44-46.
39. A. Wang, Personal communication.
40. V. Venkatasubramanian, A. Sundaram, K. Chan, J. M. Caruthers, Computer-Aided Molecular Design Using Neural Networks and Genetic Algorithms, in J. DevillerS (Ed)., Genetic Algorithms in Molecular Modelling, Academic Press Limited, New York, 1996, pp. 271-302.
41. V. Venkatasubramanian, K. Chan, J. M. Caruthers, Computer-Aided Molecular Design Using Genetic Algorithms, Computers Chem. Eng. 1995, 18, 833-844.
42. V. Venkatasubramanian, K. Chan, J. M. Caruthers, Evolutionary Design of Molecules with De-sired Properties Using a Genetic Algorithm, J. Chem. Inf. Comput. Sci. 1995, 35, 188-195.
43. A. Sundaram, V. Venkatasubramanian, Parametric Sensitivity and Search-Space Characterization Studies of Genetic Algorithms for Computer-Aided Polymer Design, / Chem. Inf. Comput. Sci. 1998, 38, 1177-1191.
44. R. B. Nachbar, Molecular Evolution: a Hierarchical Representation for Chemical Topology and its Automated Manipulation, in Proceedings of the Third Annual Genetic Programming Confer-ence, University of Wisconsin, Madison, Wisconsin, 22-25 July, 1998, pp. 246-253.
45. A. Globus, J. Lawton, T. Wipke, Automatic Molecular Design Using Evolutionary Techniques, Nanotechnology 1999, 10, 290-299.
46. R. Carhart, D. H. Smith, R. Venkataraghavan, Atom Pairs as Molecular Features in Structure-Activity Studies: Definition and Application, J. Chem. Inf. Comput. Sci. 1985, 23, 64-73.
47. J. Devillers, C. Putavy, Designing Biodegradable Molecules from the Combined Use of a Back-propagation Neural Network and a Genetic Algorithm, in J. Devillers (Ed.), Genetic Algorithms in Molecular Modelling, Academic Press Limited, New York, 1996, pp. 303-314.
48. J. Devillers, Designing Molecules with Specific Properties from Intercommunicating Hybrid Systems, /. Chem. Inf. Comput. Sci. 1996, 36, 1061-1066.
49. A. W. R. Payne, R. C. Glen, Molecular Recognition Using a Binary Genetic Search Algorithm, J. Mol. Graphics 1993,11, 74-91.
50. G. Jones, P. Willett, R. C. Glen, Genetic Algorithms for Chemical Structure Handling and Mole-cular Recognition, in J. Devillers (Ed.), Genetic Algorithms in Molecular Modelling, Academic Press Limited, New York, 1996, pp. 211-242.
51. G. Jones, P. Willett, R. C. Glen, A Genetic Algorithm for Flexible Molecular Overlay and Phar-macophore Elucidation, J. Comput.-Aided Mol. Des. 1995, 9, 532-549.
52. S. Handschuh, M. Wagener, J. Gasteiger, Superposition of Three-Dimensional Chemical Struc-tures Allowing for Conformational Flexibility by a Hybrid Method, J. Chem. Inf. Comput. Sci. 1998, 38, 220-232.
53. T. Hurst, Flexible 3-D Searching: The Directed Tweak Technique, J. Chem. Inf. Comput. Sci. 1994, 34, 190-196.
54. J. D. Holliday, P. Willett, Identification of Common Structural Features in Sets of Ligands Using a Genetic Algorithm, J. Mol. Graphics Modell. 1997, 75, 221-232.
55. D. Dolata, A. L. Parrill, W. P. Walters, CLEW: The Generation of Pharmacophore Hypotheses Through Machine Learning, SAR QSAR Environ. Res. 1998, 9, 53-81.
56. A. Leach, C. K. Prout, D. P. Dolata, Automated Conformational Analysis: Algorithms for the Efficient Construction of Low-Energy Conformations, J. Comput.-Aided Mol. Des. 1990, 4, 271-282.
57. M. Hahn, Receptor Surface Models. 1. Definition and Construction, J. Med. Chem. 1995, 38. 2080-2090.
58. H. Chen, J. Zhou, G. Xie, PARM: A Genetic Evolved Algorithm to Predict Bioactivity, J. Chem. Inf. Comput. Sci. 1998, 38, 243-250.
59. D. E. Walters, R. M. Hinds. Genetically Evolved Receptor Models: A Computational Approach to Construction of Receptor Models, J. Med. Chem. 1994, 37, 2527-2536.
60. D. E Walters, T. D. Muhammad, Genetically Evolved Receptor Models (GERM): A Procedure for the Construction of Atomic-Level Receptor Site Models in the Absence of a Receptor Crystal Structure, in Genetic Algorithms in Molecular Modelling, J. Devillers (Ed.), Academic Press Limited, New York, 1996, pp. 193-210.
61. A. Vedani, P. Zbinden, Quasi-atomistic Receptor Modeling. A Bridge Between 3-D QSAR and Receptor Fitting, Pharm. Acta Helv. 1998, 73, 11-18.