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Much of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context we call statistical molecular design, SMD. 1.
Of the thousands of novel compounds that a drug discovery project team invents and that bind to the therapeutic target, typically only a fraction of these have sufficient ADME/Tox properties to become a drug product. Understanding ADME/Tox is critical for all drug researchers, owing to its increasing importance in advancing high quality candidates to clinical studies and the processes of drug discovery. If the properties are weak, the candidate will have a high risk of failure or be less desirable as a drug product. This book is a tool and resource for scientists engaged in, or preparing for, the selection and optimization process. The authors describe how properties affect in vivo pharmacological activity and impact in vitro assays. Individual drug-like properties are discussed from a practical point of view, such as solubility, permeability and metabolic stability, with regard to fundamental understanding, applications of property data in drug discovery and examples of structural modifications that have achieved improved property performance. The authors also review various methods for the screening (high throughput), diagnosis (medium throughput) and in-depth (low throughput) analysis of drug properties. - Serves as an essential working handbook aimed at scientists and students in medicinal chemistry - Provides practical, step-by-step guidance on property fundamentals, effects, structure-property relationships, and structure modification strategies - Discusses improvements in pharmacokinetics from a practical chemist's standpoint
Recent advances in drug discovery have been rapid. The second edition of Bioinformatics and Drug Discovery has been completely updated to include topics that range from new technologies in target identification, genomic analysis, cheminformatics, protein analysis, and network or pathway analysis. Each chapter provides an extended introduction that describes the theory and application of the technology. In the second part of each chapter, detailed procedures related to the use of these technologies and software have been incorporated. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Bioinformatics and Drug Discovery, Second Edition seeks to aid scientists in the further study of the rapidly expanding field of drug discovery.
The book covers highly important topics in the challenging process from lead finding to drug candidates. Focus is upon the potential usefulness of methods for design of lead discovery libraries, lead optimisation, computational chemistry methods for the calculation of energetics of protein-ligand interaction, and computer simulations of biological activities. Important topics include new developments in chemometrics and rational molecular design as well as different aspects of structure representation, knowledge-based approaches to structure identification, and information handling.
Molecular Theory of Solvation presents the recent progress in the statistical mechanics of molecular liquids applied to the most intriguing problems in chemistry today, including chemical reactions, conformational stability of biomolecules, ion hydration, and electrode-solution interface. The continuum model of "solvation" has played a dominant role in describing chemical processes in solution during the last century. This book discards and replaces it completely with molecular theory taking proper account of chemical specificity of solvent. The main machinery employed here is the reference-interaction-site-model (RISM) theory, which is combined with other tools in theoretical chemistry and physics: the ab initio and density functional theories in quantum chemistry, the generalized Langevin theory, and the molecular simulation techniques. This book will be of benefit to graduate students and industrial scientists who are struggling to find a better way of accounting and/or predicting "solvation" properties.
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Finding the new remedy for a certain disease: an inspired goal. QSAR, an invaluable tool in drug design, aids scientists to attain this aim. This book is a long-awaited comprehensive text to QSAR and related approaches. It provides a practice-oriented introduction to the theory, methods and analyses for QSAR relationships, including modelling-based and 3D approaches. Hugo Kubinyi is a leading expert in QSAR. Readers will benefit from the author's 20 years of practical experience, from his careful calculations and recalculations of thousands of QSAR equations. Among the topics covered are: - physiocochemical parameters - quantitative models - statistical methods - Hansch analysis - Free Wilson analysis - 3D-QSAR approaches The book can readily be used as a textbook due to its high didactic value and numerous examples (over 200 equations and 1100 references).
Quantitative Structure-Activity Relationships (QSARs) are increasingly used to predict the harmful effects of chemicals to humans and the environment. The increased use of these methods in a variety of areas (academic, industrial, regulatory) results from a realization that very little toxicological or fate data is available on the vast amount of chemicals to which humans and the environment are exposed. Predicting Chemical Toxicity and Fate provides a comprehensive explanation of the state-of-the-art methods that are available to predict the effects of chemicals on humans and the environment. It describes the use of predictive methods to estimate the physiochemical properties, biological activities, and fate of chemicals. The methods described may be used to predict the properties of drugs before their development, and to predict the environmental effects of chemicals. These methods also reduce the cost of product development and the need for animal testing. This book fills an obvious need by providing a comprehensive explanation of these prediction methods. It is a practical book that illustrates the use of these techniques in real life scenarios. This book will demystify QSARs for those students unsure of them, and professionals in environmental toxicology and chemistry will find this a useful reference in their everyday working lives.
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
The gold standard for industrial research now completely revised in line with current trends in the field, with all contributions extensively updated or rewritten. In 21 chapters readers can benefit from the key working knowledge of today's leading pharmaceutical companies, including Pfizer, AstraZeneca, and Roche. Drug developers from industry and academia present all the factors governing drug bioavailability, complete with practical examples and real-life data. Part I focuses on in vitro and in vivo measurements of physicochemical properties, such as membrane permeability and ionization. Part II discusses solubility and gastrointestinal absorption, while the third part is devoted to metabolism and excretory mechanisms. The much revised and expanded part IV surveys current in silico approaches to predict drug properties needed to estimate the bioavailability of any new drug candidate. The final part shows how poor bioavailability may be improved by various approaches during the development process. No other publication offers the same level of treatment on this crucial topic in modern drug development.