Evolutionary algorithms for de novo drug design - A survey

Applied Soft Computing Journal

Volumn 27, Issue , 2015, Pages 543-552

  Devi, R Vasundhara ^a   Sathya, S Siva ^a   Coumar, Mohane Selvaraj ^a  

^a PONDICHERRY UNIVERSITY   (India)

Author keywords

Computer aided drug design; De novo drug design; Evolutionary algorithm; Genetic algorithm; Multi objective optimization; Soft computing

Indexed keywords

DRUG DELIVERY; DRUG PRODUCTS; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; SOFT COMPUTING; SURVEYS;

CHEMICAL SEARCHES; COMPUTATIONAL ASPECTS; COMPUTER-AIDED DRUG DESIGN; DRUG DESIGN; DRUG DISCOVERY PROCESS; OPTIMAL SOLUTIONS; SEARCH SPACES; SOFTCOMPUTING TECHNIQUES;

DESIGN;

EID: 84919760886     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.09.042     Document Type: Article

References (51)

1. C. Shekhar, In silico pharmacology: computer-aided methods could transform drug development, Chem. Biol. 15 (2008) 413-414.
2. A. Oduguwa, A. Tiwari, R. Roy, C. Bessant, An overview of soft computing techniques used in the drug discovery process, in: A. Abraham, B. de Baets, M. Köppen, B. Nickolay (Eds.), Applied Soft Computing Technologies: The Challenge of Complexity, Springer, Berlin, 2006, pp. 465-480.
3. T.T. Talele, S.A. Khedkar, A.C. Rigby, Successful applications of computer aided drug discovery: moving drugs from concept to the clinic, Curr. Top. Med. Chem. 10 (2010) 127-141.
4. D.E. Clark, What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov. 1 (2006) 103-110.
5. K. Loving, I. Alberts, W. Sherman, Computational approaches for fragment-based and de novo design, Curr. Top. Med. Chem. 10 (2010) 14-32.
6. G. Schneider, U. Fechner, Computer-based de novo design of drug-like molecules, Nat. Rev. Drug Discov. 4 (2005) 649-663.
7. S. Lindert, J.D. Durrant, J.A. McCammon, LigMerge: a fast algorithm to generate models of novel potential ligands from sets of known binders, Chem. Biol. Drug Des. 80 (2012) 358-365.
8. Y. Nishibata, A. Itai, Automatic creating of drug candidate structures based on receptor structure. Starting point for artificial lead generation, Tetrahedron 47 (1991) 8985-8990.
9. H.J. Bohm, LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads, J. Comput. Aided Mol. Des. 6 (1992) 593-606.
10. H.J. Bohm, The computer program LUDI: a new method for the de novo design of enzyme inhibitors, J. Comput. Aided Mol. Des. 6 (1992) 61-78.
11. V.J. Gillet, W. Newell, P. Mata, G. Myatt, S. Sike, Z. Zsoldos, A.P. Johnson, SPROUT: recent developments in the de novo design of molecules, J. Chem. Inf. Comput. Sci. 34 (1994) 207-217.
12. A.C. Pierce, G. Rao, G.W. Bemis, BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, p38, and HIV protease, J. Med. Chem. 47 (2004) 2768-2775.
13. D.E. Clark, D. Frenkel, S.A. Levy, J. Li, C.W. Murray, B. Robson, B. Waszkowycz, D.R. Westhead, PRO-LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules, J. Comput. Aided Mol. Des. 9 (1995) 13-32.
14. V. Tschinke, N.C. Cohen, The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses, J. Med. Chem. 36 (1993) 3863-3870.
15. Q. Huang, L.L. Li, S.Y. Yang, PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility, J. Mol. Graph. Model. 28 (2010) 775-787.
16. D.A. Pearlman, M.A. Murcko, CONCERTS: dynamic connection of fragments as an approach to de novo ligand design, J. Med. Chem. 39 (1996) 1651-1663.
17. P.M. Dean, S. Firth-Clark, W. Harris, S.B. Kirton, N.P. Todorov, SkelGen: a general tool for structure-based de novo ligand design, Expert Opin. Drug Discov. 1 (2006) 179-189.
18. S. Sengupta, S. Bandyopadhyay, De novo design of potential RecA inhibitors using multi objective optimization, IEEE/ACM Trans. Comput. Biol. Bioinform. 9 (2012) 1139-1154.
19. S. Bandyopadhyay, S. Saha, U. Maulik, K. Deb, A simulated-annealing based multiobjective optimization algorithm: AMOSA, IEEE Trans. Evol. Comput. 12 (2008) 269-283.
20. R. Wang, Y. Gao, L. Lai, LigBuilder: a multi-purpose program for structure-based drug design, J. Mol. Model. 6 (2000) 498-516.
21. D. Douguet, E. Thoreau, G. Grassy, A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm, J. Comput. Aided Mol. Des. 14 (2000) 449-466.
22. S.C. Pegg, J.J. Haresco, I.D. Kuntz, A genetic algorithm for structure-based de novo design, J. Comput. Aided Mol. Des. 15 (2001) 911-933.
23. N. Budin, N. Majeux, C. Tenette-Souaille, A. Caflisch, Structure-based ligand design by a build-up approach and genetic algorithm search in conformational space, J. Comput. Chem. 22 (2001) 1956-1970.
24. H.M. Vinkers, M.R. de Jonge, F.F. Daeyaert, J. Heeres, L.M. Koymans, J.H. van Lenthe, P.J. Lewi, H. Timmerman, K. Van Aken, P.A. Janssen, SYNOPSIS: SYNthesize and OPtimize System in Silico, J. Med. Chem. 46 (2003) 2765-2773.
25. D. Douguet, H. Munier-Lehmann, G. Labesse, S. Pochet, LEA3D: a computeraided ligand design for structure-based drug design, J. Med. Chem. 48 (2005) 2457-2468.
26. F. Dey, A. Caflisch, Fragment-based de novo ligand design by multiobjective evolutionary optimization, J. Chem. Inf. Model. 48 (2008) 679-690.
27. G. Schneider, M.L. Lee, M. Stahl, P. Schneider, De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks, J. Comput. Aided Mol. Des. 14 (2000) 487-494.
28. U. Fechner, G. Schneider, Flux (1): a virtual synthesis scheme for fragment-based de novo design, J. Chem. Inf. Model. 46 (2006) 699-707.
29. A. Schuller, M. Suhartono, U. Fechner, Y. Tanrikulu, S. Breitung, U. Scheffer, M.W. Gobel, G. Schneider, The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA, J. Comput. Aided Mol. Des. 22 (2008) 59-68.
30. C.A. Nicolaou, J. Apostolakis, C.S. Pattichis, De novo drug design using multiobjective evolutionary graphs, J. Chem. Inf. Model. 49 (2009) 295-307.
31. S.S. Wong, W. Luo, K.C. Chan, EvoMD: an algorithm for evolutionary molecular design, IEEE/ACM Trans. Comput. Biol. Bioinform. 8 (2011) 987-1003.
32. C.A. Nicolaou, N. Brown, Multi-objective optimization methods in drug design, Drug Discov. Today Technol. 10 (2013) e427-e435.
33. C.A. Nicolaou, N. Brown, C.S. Pattichis, Molecular optimization using computational multi-objective methods, Curr. Opin. Drug Discov. Dev. 10 (2007) 316-324.
34. G.A. Dalkas, D. Vlachakis, D. Tsagkrasoulis, A. Kastania, S. Kossida, State-of-the- art technology in modern computer-aided drug design, Brief Bioinform. 14 (2013) 745-752.
35. C.A. Nicolaou, C. Kannas, E. Loizidou, Multi-objective optimization methods in de novo drug design, Mini Rev. Med. Chem. 12 (2012) 979-987.
36. S. Binitha, S.S. Sathya, A survey of Bio inspired optimization algorithms, Int. J. Soft Comput. Eng. 2 (2012) 137-151.
37. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, 1989.
38. K. Deb, Multi-objective Optimization Using Evolutionary Algorithms, Wiley, London, 2001.
39. A. Konak, D.W. Coit, A.E. Smith, Multi-objective optimization using genetic algorithms: a tutorial, Reliab. Eng. Syst. Saf. 91 (2006) 992-1007.
40. E. Zitzler, M. Laumanns, S. Bleuler, A tutorial on evolutionary multiobjective optimization, in: X. Gandibleux, M. Sevaux, K. Sörensen, V. T'Kindt (Eds.), Lecture Notes in Economics and Mathematical Systems, Springer, Berlin, 2004, pp. 3-37.
41. N. Brown, Chemoinformatics - an introduction for computer scientists, ACM Comput. Surv. 41 (2009) 8.
42. T.J.A. Ewing, I.D. Kuntz, Critical evaluation of search algorithms for automated molecular docking and database screening, J. Comp. Chem. 18 (1997) 1175-1189.
43. M. Rarey, S. Wefing, T. Lengauer, Placement of medium-sized molecular fragments into active sites of proteins, J. Comput. Aided Mol. Des. 10 (1996) 41-54.
44. P. Kolb, A. Caflisch, Automatic and ef.cient decomposition of two-dimensional structures of small molecules for fragment-based high-throughput docking, J. Med. Chem. 49 (2006) 7384-7392.
45. N. Budin, N. Majeux, A. Caflisch, Fragment-based flexible ligand docking by evolutionary optimization, Biol. Chem. 382 (2001) 1365-1372.
46. N. Majeux, M. Scarsi, J. Apostolakis, C. Ehrhardt, A. Caflisch, Exhaustive docking of molecular fragments with electrostatic solvation, Proteins 37 (1999) 88-105.
47. P. Schneider, G. Schneider, Collection of bioactive reference compounds for focused library design, QSAR Comb. Sci. 22 (2003) 713-718.
48. G. Schneider, W. Neidhart, T. Giller, G. Schmid, Scaffold-Hopping by topological pharmacophore search: a contribution to virtual screening, Angew. Chem. Int. Ed. 38 (1999) 2894-2896.
49. S. Tietze, J. Apostolakis, GlamDock: development and validation of a new docking tool on several thousand protein-ligand complexes, J. Chem. Inf. Model. 47 (2007) 1657-1672.
50. C.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev. 46 (1997) 3-26.
51. X.Q. Lewell, D.B. Judd, S.P. Watson, M.M. Hann, RECAP - retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry, J. Chem. Inf. Comput. Sci. 38 (1998) 511-522.