Soft Computing Approaches in Chemistry

The contributions to this book cover a wide range of applications of Soft Computing to the chemical domain. The early roots of Soft Computing can be traced back to Lotfi Zadeh's work on soft data analysis [1] published in 1981. 'Soft Computing' itself became fully established about 10 years later, when the Berkeley Initiative in Soft Computing (SISC), an industrial liaison program, was put in place at the University of California - Berkeley. Soft Computing applications are characterized by their ability to: • approximate many different kinds of real-world systems; • tolerate imprecision, partial truth, and uncertainty; and • learn from their environment. Such characteristics commonly lead to a better ability to match reality than other approaches can provide, generating solutions of low cost, high robustness, and tractability. Zadeh has argued that soft computing provides a solid foundation for the conception, design, and application of intelligent systems employing its methodologies symbiotically rather than in isolation. There exists an implicit commitment to take advantage of the fusion of the various methodologies, since such a fusion can lead to combinations that may provide performance well beyond that offered by any single technique.

Invited papers with original work contributing to the evolution and use nonstandard computing methods in chemistry

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This book brings together original work from a number of authors who have made significant contributions to the evolution and use of nonstandard computing methods in chemistry and pharmaceutical industry. The contributions to this book cover a wide range of applications of Soft Computing to the chemical domain. Soft Computing applications are able to approximate many different kinds of real-world systems; to tolerate imprecision, partial truth, and uncertainty; and to learn from their environment and generate solutions of low cost, high robustness, and tractability. Presented applications are the optimization of the structure of atom clusters, the design of safe textile materials, real-time monitoring of pollutants in the workplace, quantitative structure-activity relationships, the analysis of Mössbauer spectra, the synthesis of methanol or the use of bioinformatics in the clustering of data within large biochemical databases. With this diverse range of applications, the book appeals to professionals, researchers and developers of software tools for the design of Soft Computing-based systems in chemistry and pharmaceutical industry, and to many others within the computational intelligence community.

Contenu
Application of Evolutionary Algorithms to Combinatorial Library Design.- 1 Introduction.- 2 Overview of a Genetic Algorithm.- 3 De Novo Design.- 4 Combinatorial Synthesis.- 5 Combinatorial Library Design.- 6 Reactant Versus Product Based Library Design.- 7 Reactant-Based Combinatorial Library Design.- 8 Product-Based Combinatorial Library Design.- 9 Library-Based Designs.- 10 Designing Libraries on Multiple Properties.- 11 Conclusion.- References.- Clustering of Large Data Sets in the Life Sciences.- 1 Introduction.- 2 The Grouping Problem.- 3 Unsupervised Algorithms.- 4 Supervised Algorithms.- 5 Evaluation of Clustering Results.- 6 Interpretation of Clustering Results.- 7 Conclusion.- References.- Application of a Genetic Algorithm to the refinement of complex Mössbauer Spectra.- 1 Introduction.- 2 Theoretical.- 3 Experimental.- 4 Results.- 5 Discussion.- 6 Conclusions.- References.- Soft Computing, Molecular Orbital, and Functional Theory in the Design of Safe Chemicals.- 1 Introduction.- 2 Computational Methods.- 3 Neural Network Approach.- 4 Feed-Forward Neural Network Architecture.- 5 Azo Dye Database.- 6 Concluding Remarks.- Acknowledgement.- References.- Fuzzy Logic and Fuzzy Classification Techniques.- 1 Introduction.- 2 Fuzzy Sets.- 3 Case Studies of Fuzzy Classification Techniques.- 4 Conclusion.- References.- Further Reading.- Application of Artificial Neural Networks, Fuzzy Neural Networks, and Genetic Algorithms to Biochemical Engineering.- 1 Introduction.- 2 Application of Fuzzy Reasoning to the Temperature Control of the Sake Mashing Process.- 3 Conclusion.- Acknowledgements.- References.- Genetic Algorithms for the Geometry Optimization of Clusters and Nanoparticles.- 1 Introduction: Clusters and Cluster Modeling.- 2 Overview of Applications of GAs forCluster Geometry Optimization.- 3 The Birmingham Cluster Genetic Algorithm Program.- 4 Applications of the Birmingham Cluster Genetic Algorithm Program.- 5 New Techniques.- 6 Concluding Remarks and Future Directions.- Acknowledgements.- References.- Real-Time Monitoring of Environmental Pollutants in the Workplace Using Neural Networks and FTIR Spectroscopy.- 1 Introduction.- 2 FTIR in the Detection of Pollutants.- 3 The Limitations of FTIR Spectra.- 4 Potential Advantages of Neural Network Analysis of IR Spectra.- 5 Application of the Neural Network to IR Spectral Recognition.- 6 Spectral Interpretation Using the Neural Network.- 7 Factors Influencing Network Performance.- 8 Comparison of Two and Three Layer Networks for Spectral Recognition.- 9 A Network for Analysis of the Spectrum of a Mixture of Two Compounds.- 10 Networks for Spectral Recognition and TLV Determination.- 11 Networks for Quantitative Spectral Analysis.- References.- Genetic Algorithm Evolution of Fuzzy Production Rules for the On-line Control of Phenol-Formaldehyde Resin Plants.- 1 Introduction.- 2 Resin Chemistry and Modelling.- 3 Simulation of Chemical Reactions.- 4 Model Comparison.- 5 Automated Control in Industrial Systems.- 6 Program Development.- 7 Comment.- References.- A Novel Approach to QSPR/QSAR Based on Neural Networks for Structures.- 1 Introduction.- 2 Recursive Neural Networks in QSPR/QSAR.- 3 Representational Issues.- 4 QSPR Analysis of Alkanes.- 5 QSAR Analysis of Benzodiazepines.- 6 Discussion.- 7 Conclusions.- References.- A Appendix.- Hybrid Modeling of Kinetics for Methanol Synthesis.- 1 Introduction.- 2 Neural Networks.- 3 Hybrid Modeling.- 4 Feature Selection.- 5 Modeling of Methanol Synthesis Kinetics.- 6 Conclusions.- A Appendix Analytical Model of Methanol synthesiskinetics.- Acknowledgements.- References.- About the Editors.- List of Contributors.