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An Overview of Soft Computing Techniques Used in the Drug Discovery Process

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Applied Soft Computing Technologies: The Challenge of Complexity

Part of the book series: Advances in Soft Computing ((AINSC,volume 34))

Abstract

Drug discovery (DD) research has evolved to the point of critical dependence on computerized systems, databases and newer disciplines. Such disciplines include but are not limited to bioinformatics, chemoinformatics and soft computing. Their applications range from sequence analysis methods for finding biological targets to design of combinatorial libraries in lead compound optimisation. This paper presents a brief overview of classical techniques in DD with their limitations, and outlines current SC based techniques in this area.

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References

  • Aart, E. and V. P. Laarhoven (1987). Simulated Annealing: a Review of Theory and Applications. Amsterdam, Kluwer Academic Publishers.

    Google Scholar 

  • Agostini, L. and S. Morosetti (2003). “A simple procedure to weight empirical potentials in a fitness function so as to optimise its performance in ab initio protein-folding problem.” Biophysical Chemistry 105: 105–118.

    Article  Google Scholar 

  • Bajorath, J. (2001). “Rational drug discovery revisited: interfacing experimental programs with bio- and chemo-informatics.” Drug Discovery Today 6(19): 989–995.

    Article  Google Scholar 

  • Bamborough, P. and F. E. Cohen (1996). “Modelling Protein-Ligand complexes.” Current Opinion in Structural Biology 6: 236–241.

    Article  Google Scholar 

  • Bleicher, K. H., H. Bohm, et al. (2003). “Hit and Lead Generation: Beyond High-Throughput Screening.” Nature Review Drug Discovery 2(5): 369–378.

    Article  Google Scholar 

  • Brown, R. D., G. Jones, et al. (1994). “Matching two-dimensional chemical graphs using genetic algorithms.” Journal of Chemical Information and Computer Sciences 34(1): 63–67.

    Article  Google Scholar 

  • Budin, N., S. Ahmed, et al. (2001). “An Evolutionary Approach for Structurebased Design of Natural and Non-natural Peptidic Ligands.” Combinatorial Chemistry and HTS 4: 661–673.

    Google Scholar 

  • Budin, N., N. Majeux, et al. (2001). “Structure-based Ligand Design by a Build-up Approach and Genetic Algorithm Search in Conformational Space.” Journal of Computational Chemistry(22): 1956–1970.

    Google Scholar 

  • Castrodale, B. (2002). Leading Genomic Approaches for Breaking Bottlenecks in Drug Discovery and Development. Massachusetts, Cambridge Healthtech Institute: 1–7.

    Google Scholar 

  • Chanda, S. K. and J. S. Caldwell (2003). “Fulfilling the promise: drug discovery in the post-genomic era.” Drug Discovery Today 8(4): 168–174.

    Article  Google Scholar 

  • Chang, B. C. H. and S. K. Halgamuge (2002). “Protein Motif Extraction with Neuro-Fuzzy Optimization.” Bioinformatics 18(8): 1084–1090.

    Article  Google Scholar 

  • Clark, D. E., G. Jones, et al. (1994). “Pharmacophoric pattern matching in files of three-dimensional chemical structures: Comparison of conformational-searching algorithms for flexible searching.” Journal of Chemical Information and Computer Sciences 34(1): 197–206.

    Article  Google Scholar 

  • Clark, D. E. and S. D. Pickett (2000). “Computational methods for the prediction of ‘drug-likeness’.” Drug Discovery Today 5(2): 49–57.

    Article  Google Scholar 

  • Cooper, L. R., D. W. Corne, et al. (2003). “Use of a novel Hill-Climbing genetic algoriothm in protein folding simulations.” Computational Biology and Chemistry 27: 575–580.

    Article  Google Scholar 

  • Desjarlais, J. R. and N. D. Clarke (1998). “Computer search algorithms in protein modification and design.” Current opinion in Structural Biology 8: 471–475.

    Article  Google Scholar 

  • Deutsch, J. M. (2003). “Evolutionary Algorithms for Finding Optimal Gene Sets in Microaray Prediction.” Bioinformatics 19(1): 45–52.

    Article  Google Scholar 

  • FitzGerald, K. (2000). “In vitro display technologies – new tools for drug discovery.” DDT 5(6): 253–258.

    Google Scholar 

  • Fontain, E. (1992). “Application of genetic algorithms in the field of constitutional similarity.” Journal of Chemical Information and Computer Sciences 32(1): 748–752.

    Article  Google Scholar 

  • Glen, R. C. and A. W. R. Payne (1995). “A genetic algorithm for the automated generation of molecules within constraints.” Journal of Computer-Aided Molecular Design. 9(2): 181–202.

    Article  Google Scholar 

  • Globus, A. L. J. et al. (1999). “Automatic molecular design using evolutionary techniques.” Nanotechnology 10: 290–299.

    Article  Google Scholar 

  • Hanada, K., T. Yokoyama, et al. (2000). Multiple Sequence Alignment by Genetic Algorithm. Genome Informatics. 11: 317–318.

    Google Scholar 

  • Hillisch, A. and R. Hilgenfield (2003). Modern Methods of Drug Discovery. Springer Verlag.

    Google Scholar 

  • Horng, J., L. Wu, et al. (2004). “A genetic algorithm for mutiple sequnce alignment.” Soft Computing.

    Google Scholar 

  • Illgen, K., T. Enderle, et al. (2000). “Simulated molecular evolution in a full combinatorial library.” Chemisry and Biology 7: 433–441.

    Article  Google Scholar 

  • Isokawa, M., M. Wayama, et al. (1996). “Multiple Sequence Alignment Using Genetic Algorithm.” Genome Informatics 7: 176–177.

    Google Scholar 

  • Jagla, B. and J. Schuchhardt (2000). “Adaptive Encoding Neural Networks for the Recognition of Human Signal Peptide Cleavage Sites.” Bioinformatics 16: 245–250.

    Article  Google Scholar 

  • Jones, G., P. Willett, et al. (1995). “Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation.” Journal of Molecular biology 245: 43–53.

    Article  Google Scholar 

  • Jue, R. A., N. W. Woodbury, et al. (1980). “Sequence homologies among e. coli ribosomal proteins: evidence for evolutionary related groupings and internal duplications.” Journal of Molecular Evolution 15: 129–148.

    Article  Google Scholar 

  • Keedwell, E. C. and A. Narayanan (2003). Genetic algorithms for gene expression analysis. Applications of Evolutionary Computation: Proceedings of the 1st European Workshop on Evolutionary Bioinformatics(EvoBIO 2003), Springer Verlag LNCS.

    Google Scholar 

  • Khan, J., J. S. Wei, et al. (2001). “Classification and diagnostic prediction of cancers using gene expression profilling and artificial neuarl networks.” nature Medicine 7(6): 673–679.

    Article  Google Scholar 

  • Knowles, J. and G. Gromo (2002). “Target Selection in Drug Discovery.” Nature Reviews: Drug Discovery 2: 63–69.

    Article  Google Scholar 

  • Konig, R. and T. Dandekar (1999). “Improving genetic algorithms for protien folding simulations by systematic crossover.” BioSystems 50: 17–25.

    Article  Google Scholar 

  • Langdon, W. B., S. J. Barret, et al., Eds. (2002). Genetic Programming for combining neural networks for drug discovery. Soft Computing and Industrial Application, Springer-Verlag.

    Google Scholar 

  • Lawrence, C., S. Altschul, et al. (1993). “Detecting subtle sequence signals: a gibbs sampling strategy for multiple alignment.” Science 262: 208–214.

    Google Scholar 

  • Ma, C. (2004). Animal models of diseases. Mdern Drug Discovery. 3: 30–36.

    MATH  Google Scholar 

  • Maggio, E. T. and K. Ramnarayan (2001). “Recent developments in computational proteomics.” Drug Discovery Today 6(19): 996–1004.

    Article  Google Scholar 

  • Manallack, D. T. and D. J. Livingstone (1999). “Neural networks in drug discovery: have they lived up to their promise?” Europen Jopurnal of Medicinal Chemistry 34: 195–208.

    Article  Google Scholar 

  • Marton, M. J., J. L. DeRisi, et al. (1998). “Drug target validation and identification of secondary drug target effects using DNA microarrays.” Nature Medicine 4(11): 1293–1301.

    Article  Google Scholar 

  • Needleman, S. B. and C. D. Wunsch (1970). “A general method applicable to the search for similarities in the amino acid sequences of two proteins.” Journal of Molecular Biology 42: 245–261.

    Google Scholar 

  • Notredame, C. and D. G. Higgins (1996). “SAGA: sequence alignment by genetic algorithm.” Nucleic Acids Research 24(8): 1515–1524.

    Article  Google Scholar 

  • Oduguwa, V. (2003). Rolling System Design Optimisation Using Soft Computing Techniuques. Enterprise Integration. Bedfordshire, Cranfield: 332.

    Google Scholar 

  • Ooi, C. H. and P. Tan (2003). “Genetic Algorithms Applied to Multi-Class prediction for the Analysis of Gene Expression Data.” Bioinformatics 19(1): 37–44.

    Article  Google Scholar 

  • Oshiro, C. M., I. D. Kuntz, et al. (1995). “Flexible ligand docking using a genetic algorithm.” Journal of Computer-Aided Molecular Design 9(1): 113–130.

    Article  Google Scholar 

  • Parrill, A. (1996). “Evolutionary and genetic methods in drug design.” DDT 1(12): 514–521.

    Google Scholar 

  • Pedersen, J. T. and J. Moult (1996). “Genetic algorithms for protein structure prediction.” Current Opinion in Structural Biology 6: 227–231.

    Article  Google Scholar 

  • Pedersen, J. T. and J. Moult (1997). “Protein Folding simulations with genetic algorithms and a detailed description.” Journal of Molecular Biology 269: 240–259.

    Article  Google Scholar 

  • Pegg, S. C. H., J. J. Haresco, et al. (2001). “A Genetic Algorithm for Structurebased De Novo Design.” Journal of Computer-Aided Molecular Design. 15: 911–933.

    Article  Google Scholar 

  • Reijmers, T. H., R. Wehrens, et al. (1999). “Quality Criteria of Genetic Algorithm for Construction of Phylogenetic Trees.” Journal of Computational Chemistry 20(8): 867–876.

    Article  Google Scholar 

  • Schneider, G., O. Clement-Chomienne, et al. (2000). “Virtual Screening for Bioactive Molecules by Evolutionary De Novo Design.” Angewandte Chemie International Edition in English 39: 4130–4133.

    Article  Google Scholar 

  • Schneider, G., M.-L. Lee, et al. (2000). “De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks.” Journal Computer-Aided Molecular Design 14: 487–494.

    Article  Google Scholar 

  • Searls, D. B. (2000). “Using Bioinformatics in gene and drug discovery.” DDT. 5(4): 135–143.

    Google Scholar 

  • Stahura, F. L. and J. Bajorath (2002). “Bio- and chemo-informtics beyond data management: crucial challenges and future opportunities.” Drug Discovery Today 7(11): s41–s47.

    Article  Google Scholar 

  • Swindells, M. B. and J. P. Overington (2002). “Prioritizing the proteome: identifying pharmaceutically relevant targets.” Drug Discovery Today 7(9): 516–521.

    Article  Google Scholar 

  • Theullon-Sayag, V. (2002). Impact of e-Pharma Technology on the Drug Development Process. Enterprise Integration. Cranfield, Cranfield University: 93.

    Google Scholar 

  • Verkman, A. S. (2004). “Drug Discovery in academia.” American Journal of Physiology – Cell Physiology 286: C465–C474.

    Article  Google Scholar 

  • Wang, R., Y. Gao, et al. (2000). “LigBuilder: A Multi-Purpose Program for Structure-based Drug Design.” Journal of Molecular Modeling 6: 498–516.

    Article  MATH  Google Scholar 

  • Weber, L. (1998). “Application of genetic algorithms in molecular diversity.” Currnet Opinion in Chemical Biology 2: 381–385.

    Article  Google Scholar 

  • Weber, L. (1998). “Evolutionary combinatorial chemistry: application of genetic algoithms.” Drug discovery Today 3(8): 379–385.

    Article  Google Scholar 

  • Weber, L., S. Wallbaum, et al. (1995). “Optimisation of the Biological Activity of Combinatorial Compound Libraries by a Genetic Algorithm.” Angewandte Chemie International Edition in English 34: 2280–2282.

    Article  Google Scholar 

  • Wild, D. J. and P. Willett (1996). “Similarity Searching in Files of Three-Dimensional Chemical Structures. Alignment of Molecular Electrostatic Potential Fields with a Genetic Algorithm.” Journal of Chemical Information and Computer Sciences 36(2): 159–167.

    Article  Google Scholar 

  • Winkkler, D. A. and F. R. Burden (2004). “Bayesian neural nets for modeling in drug discovery.” DDT: BIOSILICO 2(3): 104–111.

    Article  Google Scholar 

  • Yang, J.-M. and C.-C. Chen (2004). “GEMDOCK: A generic evolutionary method for molecular docking.” PROTEINS: Structure. Function, and Bioinformatics 55: 288–304.

    Article  Google Scholar 

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Oduguwa, A., Tiwari, A., Roy, R., Bessant, C. (2006). An Overview of Soft Computing Techniques Used in the Drug Discovery Process. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_36

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  • DOI: https://doi.org/10.1007/3-540-31662-0_36

  • Publisher Name: Springer, Berlin, Heidelberg

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