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As analysis, in terms of detection limits and technological innovation, in chemical and biological fields has developed so computational techniques have advanced enabling greater understanding of the data. Indeed, it is now possible to simulate spectral data to an excellent level of accuracy, allowing chemists and biologists access to robust and reliable analytical methodologies both experimentally and theoretically. This work will serve as a definitive overview of the field of computational simulation as applied to analytical chemistry and biology, drawing on recent advances as well as describing essential, established theory. Computational approaches provide additional depth to biochemical problems, as well as offering alternative explanations to atomic scale phenomena. Highlighting the innovative and wide-ranging breakthroughs made by leaders in computational spectrum prediction and the application of computational methodologies to analytical science, this book is for graduates and postgraduate researchers showing how computational analytical methods have become accessible across disciplines. Contributed chapters originate from a group of internationally-recognised leaders in the field, each applying computational techniques to develop our understanding of and supplement the data obtained from experimental analytical science.
The cost of drug development is increasing, and investment returns are decreasing. The number of drugs approved by FDA is in decline in terms of the number of new molecular entities (NMEs). Amongst the reasons noted for this are the adverse side effects and reduced efficiency of many of the potential compounds. This is a problem both for the pharmaceutical industry and for those suffering from diseases for which there are no or few available treatments. Advances in computational chemistry, computer science, structural biology and molecular biology have all contributed to improved drug design strategies and reduced the time taken for drug discovery. By interfacing cheminformatics and bioinformatics with systems biology we can create a powerful tool for understanding the mechanisms of patho-physiological systems and identifying lead molecules for various diseases. This integration of drug design approaches can also play a crucial role in the prediction and rationalization of drug effects and side effects, improving safety and efficacy and leading to better approval rates. Addressing the lack of knowledge on the fundamental aspects of the various computational tools for drug discovery, this book is a compilation of recent bioinformatics and cheminformatics approaches, and their integration with systems biology. Written primarily for researchers and academics in chem- and bioinformatics, it may also be a useful resource for advanced-level students.
Theoretical and Computational Photochemistry: Fundamentals, Methods, Applications and Synergy with Experimental Approaches provides a comprehensive overview of photoactive systems and photochemical processes. After an introduction to photochemistry, the book discusses the key computational chemistry methods applied to the study of light-induced processes over the past decade, and further outlines recent research topics to which these methods have been applied. By discussing the synergy between experimental and computational data, the book highlights how theoretical studies could facilitate understanding experimental findings. This helpful guide is for both theoretical chemists and experimental photochemistry researchers interested in utilizing computational photochemistry methods for their own work. - Reviews the fundamentals of photochemistry, helping those new to the field in understanding key concepts - Provides detailed guidance and comparison of computational and theoretical methods, highlighting the suitability of each method for different case studies - Outlines current applications to encourage discussion of the synergy between experimental and computational data, and inspiring further application of these methods to other photochemical processes
Over the last decade, there has been a significant shift from traditional mechanistic and empirical modelling into statistical and data-driven modelling for applications in reaction engineering. In particular, the integration of machine learning and first-principle models has demonstrated significant potential and success in the discovery of (bio)chemical kinetics, prediction and optimisation of complex reactions, and scale-up of industrial reactors. Summarising the latest research and illustrating the current frontiers in applications of hybrid modelling for chemical and biochemical reaction engineering, Machine Learning and Hybrid Modelling for Reaction Engineering fills a gap in the methodology development of hybrid models. With a systematic explanation of the fundamental theory of hybrid model construction, time-varying parameter estimation, model structure identification and uncertainty analysis, this book is a great resource for both chemical engineers looking to use the latest computational techniques in their research and computational chemists interested in new applications for their work.
Focusing on key methodological breakthroughs in the field, this book provides newcomers with a comprehensive menu of multiscale modelling options.
Written chemical formulas, such as C2H6O, can tell us the constituent atoms a molecule contains but they cannot differentiate between the possible geometrical arrangements (isomers) of these models. Yet the chemical properties of different isomers can vary hugely. Therefore, to understand the world of chemistry we need to ask what kind of isomers can be produced from a given atomic composition, how are isomers converted into each other, how do they decompose into smaller pieces, and how can they be made from smaller pieces? The answers to these questions will help us to discover new chemistry and new molecules. A potential energy surface (PES) describes a system, such as a molecule, based on geometrical parameters. The mathematical properties of the PES can be used to calculate probable isomer structures as well as how they are formed and how they might behave. Exploration on Quantum Chemical Potential Energy Surfaces focuses on the PES search based on quantum chemical calculations. It describes how to explore the chemical world on PES, discusses fundamental methods and specific techniques developed for efficient exploration on PES, and demonstrates several examples of the PES search for chemical structures and reaction routes.
Analytical Techniques in Biosciences: From Basics to Applications presents comprehensive and up-to-date information on the various analytical techniques obtainable in bioscience research laboratories across the world. This book contains chapters that discuss the basic bioanalytical protocols and sample preparation guidelines. Commonly encountered analytical techniques, their working principles, and applications were presented. Techniques, considered in this book, include centrifugation techniques, electrophoretic techniques, chromatography, titrimetry, spectrometry, and hyphenated techniques. Subsequent chapters emphasize molecular weight determination and electroanalytical techniques, biosensors, and enzyme assay protocols. Other chapters detail microbial techniques, statistical methods, computational modeling, and immunology and immunochemistry.The book draws from experts from key institutions around the globe, who have simplified the chapters in a way that will be useful to early-stage researchers as well as advanced scientists. It is also carefully structured and integrated sequentially to aid flow, consistency, and continuity. This is a must-have reference for graduate students and researchers in the field of biosciences. - Presents basic analytical protocols and sample-preparation guidelines - Details the various analytical techniques, including centrifugation, spectrometry, chromatography, and titrimetry - Describes advanced techniques such as hyphenated techniques, electroanalytical techniques, and the application of biosensors in biomedical research - Presents biostatistical tools and methods and basic computational models in biosciences
Over the past decade, great strides have been taken in developing methodologies that can treat more and more complex nano- and nano-bio systems embedded in complex environments. Multiscale Dynamics Simulations covers methods including DFT/MM-MD, DFTB and semi-empirical QM/MM-MD, DFT/MMPOL as well as Machine-learning approaches to all of the above. Focusing on key methodological breakthroughs in the field, this book provides newcomers with a comprehensive menu of multiscale modelling options so that they can better chart their course in the nano/bio world.
Computational Quantum Chemistry presents computational electronic structure theory as practised in terms of ab initio waveform methods and density functional approaches. Getting a full grasp of the field can often prove difficult, since essential topics fall outside of the scope of conventional chemistry education. This professional reference book provides a comprehensive introduction to the field. Postgraduate students and experienced researchers alike will appreciate Joseph McDouall's engaging writing style. The book is divided into five chapters, each providing a major aspect of the field. Electronic structure methods, the computation of molecular properties, methods for analysing the output from computations and the importance of relativistic effects on molecular properties are also discussed. Links to the websites of widely used software packages are provided so that the reader can gain first hand experience of using the techniques described in the book.