In Silico Chemistry and Biology: Current and Future Prospects provides a compact overview on recent advances in this highly dynamic branch of chemistry. Various methods of protein modelling and computer-assisted drug design are presented, including fragment- and ligand-based approaches. Many successful practical applications of these techniques are demonstrated. The authors also look to the future and describe the main challenges of the field.
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.
Over the past years, the chem(o)informatics field has further evolved and new application areas have opened up, for example, in the broadly defined area of chemical biology. In Chemoinformatics and Computational Chemical Biology, leading investigators bring together a detailed series of reviews and methods including, among others, system-directed approaches using small molecules, the design of target-focused compound libraries, the study of molecular selectivity, and the systematic analysis of target-ligand interactions. Furthermore, the book delves into similarity methods, machine learning, probabilistic approaches, fragment-based methods, as well as topics that go beyond the current chemoinformatics spectrum, such as knowledge-based modeling of G protein-coupled receptor structures and computational design of siRNA libraries. As a volume in the highly successful Methods in Molecular BiologyTM series, this collection provides detailed descriptions and implementation advice that are exceedingly relevant for basic researchers and practitioners in this highly interdisciplinary research and development area. Cutting-edge and unambiguous, Chemoinformatics and Computational Chemical Biology serves as an ideal guide for experts and newcomers alike to this vital and dynamic field of study.
Computational Chemistry Methodology in Structural Biology and Materials Sciences provides a selection of new research in theoretical and experimental chemistry, focusing on topics in the materials science and biological activity. Part 1, on Computational Chemistry Methodology in Biological Activity, of the book emphasizes presents new developments in the domain of theoretical and computational chemistry and its applications to bioactive molecules. It looks at various aspects of density functional theory and other issues. Part 2, on Computational Chemistry Methodology in Materials Science, presents informative new research on computational chemistry as applied to materials science. The wide range of topics regarding the application of theoretical and experimental chemistry and materials science and biological domain will be valuable in the context of addressing contemporary research problems.
Biological and chemical sciences have undergone an unprecedented transformation, reflected by the huge use of parallel and automated technologies in key fields such as genome sequencing, DNA chips, nanoscale functional biology or combinatorial chemistry. It is now possible to generate and store from tens of thousands to millions of new small molecules, based on enhanced chemical synthesis strategies. Automated screening of small molecules is one of the technologies that has revolutionized biology, first developed for the pharmaceutical industry and recently introduced in academic laboratories. High-throughput and high-content screening allow the identification of bioactive compounds in collections of molecules (chemical libraries), being effective on biological targets defined at various organisational scales, from proteins to cells to complete organisms. These bioactive molecules can be therapeutic drug candidates, molecules for biotech, diagnostic or agronomic applications, or tools for basic research. Handling a large number of biological (genomic and post-genomic), chemical and experimental information, screening approaches cannot be envisaged without any electronic storage and mathematical treatment of the data. “Chemogenomics and Chemical Genetics” is an introductory manual presenting methods and concepts making up the basis for this recent discipline. This book is dedicated to biologists, chemists and computer scientist beginners. It is organized in brief, illustrated chapters with practical examples. Clear definitions of biological, chemical and IT concepts are given in a glossary section to help readers who are not familiar with one of these disciplines. "Chemogenomics and Chemical Genetics" should therefore be helpful for students (from Bachelor's degree level), technological platform engineers, and researchers in biology, chemistry, bioinformatics, cheminformatics, both in biotech and academic laboratories.
Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.
Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter. Sample Chapter(s). Chapter 1: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (160 KB). Contents: Overview: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (H Tang & S Kim); An Introduction to Soft Computing (A Konar & S Das); Biological Sequence and Structure Analysis: Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound (J E Gallardo et al.); Classification of RNA Sequences with Support Vector Machines (J T L Wang & X Wu); Beyond String Algorithms: Protein Sequence Analysis Using Wavelet Transforms (A Krishnan & K-B Li); Filtering Protein Surface Motifs Using Negative Instances of Active Sites Candidates (N L Shrestha & T Ohkawa); Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction (G Pollastri et al.); In Silico Design of Ligands Using Properties of Target Active Sites (S Bandyopadhyay et al.); Gene Expression and Microarray Data Analysis: Inferring Regulations in a Genomic Network from Gene Expression Profiles (N Noman & H Iba); A Reliable Classification of Gene Clusters for Cancer Samples Using a Hybrid Multi-Objective Evolutionary Procedure (K Deb et al.); Feature Selection for Cancer Classification Using Ant Colony Optimization and Support Vector Machines (A Gupta et al.); Sophisticated Methods for Cancer Classification Using Microarray Data (S-B Cho & H-S Park); Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data (A Mukhopadhyay et al.). Readership: Graduate students and researchers in computer science, bioinformatics, computational and molecular biology, artificial intelligence, data mining, machine learning, electrical engineering, system science; researchers in pharmaceutical industries.
This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs.
Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. - Provides practical information on how to choose and use appropriate computational tools - Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format - Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics
