Optimization algorithms and natural computing in drug discovery

Drug Discovery Today: Technologies

Volumn 1, Issue 3, 2004, Pages 247-252

  Solmajer, Tom ^a,b   Zupan, Jure ^a  

^a NATIONAL INSTITUTE OF CHEMISTRY   (Slovenia)

^b Lek dd   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG;

ALGORITHM; ANALYTIC METHOD; ARTIFICIAL NEURAL NETWORK; BINDING AFFINITY; CHEMOMETRIC ANALYSIS; COMBINATORIAL CHEMISTRY; COMPUTER MODEL; COMPUTER SIMULATION; COST; DRUG IDENTIFICATION; DRUG SCREENING; DRUG TARGETING; ENERGY; GENETIC ALGORITHM; HIGH THROUGHPUT SCREENING; IN VITRO STUDY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MOLECULAR MODEL; MONTE CARLO METHOD; PROCESS OPTIMIZATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; REVIEW; STRUCTURE ANALYSIS; VIRTUAL REALITY;

EID: 13344285267     PISSN: 17406749     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ddtec.2004.11.011     Document Type: Review

References (33)

1. J. Zupan J. Gasteiger Neural Networks in Chemistry and Drug Design 1999 Wiley-VCH
2. L. Davis (ed.) Handbook of Genetic Algorithms 1991 Van Nostrand Reinhold pp. 1-101
3. R-G. Brereton Chemometrics. In Data Analysis for the Laboratory and Chemical Plant 2003 John Wiley
4. D.L. Massart et al. Handbook of Chemometrics and Qualimetrics: Part A 1997 Elsevier
5. B.G.M. Vandengiste et al. Handbook of Chemometrics and Qualimetrics: Part B 1998 Elsevier
6. J.H. Holland Adaption in Natural and Artificial Systems 1992 MIT Press (revised print)
7. D. Vanderbilt S.G. Louie J. Comp. Phys. 56 1984 259-271
8. S. Kirckpatrick et al. Optimisation by simulated annealing Science 220 1983 671-680
9. N. Metropolis et al. Equation of State calculation by fast computing machines J. Chem. Phys. 21 1953 1087-1091
10. J.A. Caruna J.D. Schaffer Representation and hidden bias: Gray vs. binary coding for genetic algorithms. In: J. Laird (Ed.) Proceedings of the IMCL-88 International Conference on Machine Learning, Morgan Kauffman 1988
11. T. Kohonen Self-organization and Associative Memory 3rd edn. 1989 Springer Verlag
12. D.E. Rumelhart et al. Learning internal representations by error back-propagation. In: D.E. Rumelhart J.L. MacClelland (Eds.) Distributed Parallel Processing: Explorations in the Microstructures of Cognition Vol. 1 1986 MIT Press 318-362
13. R. Hecht-Nielsen Application of counter-propagation networks Neural Netw. 1 1988 131-140
14. J. Zupan et al. Kohonen and counter-propagation artificial neural networks in analytical chemistry Chemom. Intell. Lab. Syst. 38 1997 1-23
15. M. Novic et al. Quantitative structure-activity relationship of flavonoid p56(lck) protein tyrosine kinase inhibitors. A neural network approach J. Chem. Inf. Comput. Sci. 37 1997 990-998
16. G. Mlinsek et al. Prediction of enzyme binding: Human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures J. Chem. Inf. Comput. Sci. 41 2001 1286-1294
17. G. Mlinsek et al. Enzyme binding selectivity prediction: α-thrombin vs. trypsin inhibition J. Chem. Inf. Comput. Sci. 44 2004 1872-1882
18. B. Walczak D.L. Massart Application of radial basis functions - partial least squares to non-linear pattern recognition problems: Diagnosis of process faults Anal. Chim. Acta 331 1996 177-185
19. S. Renals Radial basis function network for speech pattern classification Electron. Lett. 25 1989 437-439
20. S. Izrailev D. Agrafiotis A novel method for building regression tree models for qsar based on artificial ant colony systems J. Chem. Inf. Comput. Sci. 41 2001 176-180
21. M. Dorigo L.M. Gambardella IEEE Trans. Evol. Comput. 1 1997 53-66
22. H-J. Bohm G. Schneider Virtual Screening for Bioactive Molecules 2000 Wiley-VCH
23. G. Klebe Virtual Screening: An Alternative or Complement to High Throughput Screening 2000 Kluwer
24. I. Halperin et al. Principles of docking: An overview of search algorithms and a guide to scoring functions Proteins 47 2002 409-443
25. G.M. Morris et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function J. Comp. Chem. 19 1998 1639-1662
26. T.J.A. Ewing I.D. Kuntz Critical evaluation of search algorithms for automated molecular docking and database screening J. Comput. Chem. 18 1997 1175-1189
27. N. Rhodes et al. CLIP: Similarity searching of 3D databases using clique detection J. Chem. Inf. Comput. Sci. 43 2003 443-448
28. R. Abagyan M. Totrov High-throughput docking for lead generation Curr. Opin. Chem. Biol. 5 2001 375-382
29. H. Gohlke G. Klebe Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors Angew. Chem. Int. Ed. 41 2002 2644-2676
30. L. Brive R. Abagyan Computational structural proteomics. In: W. Seidel H-W. Mewes (Eds.) Bioinformatics and Genome Analysis 2002 Springer-Verlag 149-166
31. R.C. Glen S.C. Allen Ligand-protein docking: Cancer research at the interface between biology and chemistry Curr. Med. Chem. 10 2003 763-777
32. Z. Deng et al. Structural interaction fingerprint (SIFt): A novel method for analyzing three-dimensional protein-ligand binding interactions J. Med. Chem. 47 2004 337-344
33. F. Daeyaert et al. A pharmacophore docking algorithm and its application to the cross-docking of 18 HIV-NNRTI's in their binding pocket proteins Struct. Funct. Genomics 54 2004 526-533