Complexity in Computational Chemistry

This volume of the Encyclopedia of Complexity and Systems Science Series describes the current state of the analysis of complexity in chemistry from a wide-ranging perspective. The volume covers not only the classical areas of molecular complexity and its implications in molecular design, QSAR, QSPR and molecular evolution, but also topics at the very forefront of development of this field in cross-disciplinary areas. The latter include for instance, chaotic systems in biochemistry as well as biological complexity and self-organization, topics related to organizational complexity at the nanoscale, as well as the structural and dynamical complexity of giant systems of interacting molecules, such as protein residue networks, protein-protein interaction networks and metabolic networks. All these topics are interwoven by a general overarching concept of complexity. Thus, from a theoretical point of view, the reader benefits from a broad view of the analysis of complexity in many different types of molecular and chemical systems. In addition, the reader gains insight into a series of modern techniques and tools that allow for a practical understanding of these complex chemical systems. Such modern techniques include the use of cellular automata, design of reaction sites, artificial intelligence methods, machine learning, quantum similarity techniques, network-theoretic, informational tools, QSAR, QSPR and molecular design, among others.
Complexity in Computational Chemistry is a valuable reference for molecular biologists, chemists, molecular engineers, computer scientists and mathematicians interested in the study of chemical complexity at any scale, from the atomic to the intermolecular.

Written by leading experts from around the world on chemical and biochemical complexity

Serves as a comprehensive reference for anyone interested in the study of chemical complexity from the atomic to the intermolecular level

Thoroughly updated from the first edition of the Encyclopedia--including new chapters on complexity in proteins, protein-protein interactions and metabolic transformation networks

Auteur

Professor Ernesto Estrada is currently Chair of Complexity Sciences at the University of Strathclyde. His research has shaped and developed the study of complex networks and of mathematical chemistry. His expertise ranges from the areas of network structure, algebraic network theory, dynamical systems on networks to the study of random models of networks and the study of graph invariants for studying molecules, QSAR, QSPR and drug design. He has 188 publications attracting more than 9,200 citations, and a h-index of 54. He has published three books on network science, as well as 15 refereed book chapters. He is the founder and Editor-in-Chief of the Journal of Complex Networks, and Associate Editor of the SIAM Journal of Applied Mathematics, and he is on the Editorial Board of MATCH: Communications in Mathematical and in Computer Chemistry.

Contenu

1. Chaotic Dynamics, Noise, and Fractal Space in Biochemistry2. Biological Complexity and Biochemical Information3. Complexity and Self-organization in Biological Development and Evolution4. Cellular Automata Modeling of Complex Biochemical Systems5. Multifunctional Composites6. Introduction to Complexity in Computational Chemistry7. Computer-Aided Design of the Reaction Site in Heterogeneous Catalysis8. DNA-Templated Self-Assembly of Protein Arrays and Highly Conductive Nanowires9. Drug Design with Artificial Intelligence Methods10. Drug Design with Artificial Neural Networks11. Drug Design with Machine Learning12. Molecular Descriptors in Drug Design13. Information Theoretic Complexity Measures14. Networks in Molecular Evolution15. Complexity of Nanoscale Atomic Clusters16. Nonlinearity in Polymers17. Complexity and Challenges of Modern QSAR Modeling and QSAR Based Virtual Screening18. Quantum Similarity and Quantum Quantitative Structure-Properties Relationships (QQSPR)19. Self-assembled Materials20. Topological Complexity of Molecules21. Complexity of Protein residue networks22. Complexity of Protein-protein interaction networks23. Complexity of Metabolic networks