Multidimensional Pharmacochemistry: Design of Safer Drugs deals with techniques based on the theory of simultaneous statistical inference and the qualitative rules that can be applied in solving problems of high toxicity. This book points out that the multidimensional view of data analysis can be applied to solve problems in medicinal chemistry. Investigators use different approaches; a certain procedure can prove to be the most beneficial for a specific drug design. This text presents the theoretical assumptions that mathematicians make to derive the basis for their multivariate techniques. This book also describes, in nonmathematical terms, a set of methods that are valuable, as well as explain the different designs by using numerical examples. According to E.J. Ariens, drug action involves the pharmaceutical, pharmacokinetic-toxokinetic, and pharmacodynamics-toxodynamic phases. The multivariate structure-activity analysis (MASCA) Model of Pharmacochemistry is a highly unified multivariate approach to drug design. To develop a multidimensionally oriented pharmacology, the book notes that the investigator can use the "dynamic structure-activity analysis." This entails the experimentalist and chemist using quantitative approaches and intuitive elements from a small number of compounds toward larger groups, with successive changes being inputted in the desired biological activity. This book is strongly recommended for toxicologists, pharmacologists, applied mathematicians, medicinal and agricultural chemists.

This volume is intended to be a guide to advanced students and researchers regarding the various computer-aided statistical techniques that may be used to plan, design and correct experiments in current medicinal and agricultural chemistry research. These techniques are described first in theory and then in practical application with the use of actual data taken from real experiments. The book presupposes some knowledge of statistics and elementary matrix algebra.

Reporting the rapidly growing field of rational drug design, this work is composed from a selected, topical range of chapters written by specialists in each field.

Progress in medicinal chemistry and in drug design depends on our ability to understand the interactions of drugs with their biological targets. Classical QSAR studies describe biological activity in terms of physicochemical properties of substituents in certain positions of the drug molecules. The purpose of this book is twofold: On the one hand, both the novice and the experienced user will be introduced to the theory and application of 3D QSAR analyses, and on the other, a comprehensive overview of the scope and limitations of these methods is given. The detailed discussion of the present state of the art should enable scientists to further develop and improve these powerful new tools. The greater part of the book is dedicated to the theoretical background of 3D QSAR and to a discussion of CoMFA applications. In addition, various other 3D QSAR approaches and some CoMFA-related methods are described in detail. Thus, the book should be valuable for medicinal, agricultural and theoretical chemists, biochemists and biologists, as well as for other scientists interested in drug design. Its content, starting at a very elementary level and proceeding to the latest methodological results, the strengths and limitations of 3D QSAR approaches, makes the book also appropriate as a text for teaching and for graduate student courses.

Statistical Factor Analysis and Related Methods Theory andApplications In bridging the gap between the mathematical andstatistical theory of factor analysis, this new work represents thefirst unified treatment of the theory and practice of factoranalysis and latent variable models. It focuses on such areasas: * The classical principal components model and sample-populationinference * Several extensions and modifications of principal components,including Q and three-mode analysis and principal components in thecomplex domain * Maximum likelihood and weighted factor models, factoridentification, factor rotation, and the estimation of factorscores * The use of factor models in conjunction with various types ofdata including time series, spatial data, rank orders, and nominalvariable * Applications of factor models to the estimation of functionalforms and to least squares of regression estimators

Divided into five major parts, the two volumes of this ready reference cover the tailoring of theoretical methods for biochemical computations, as well as the many kinds of biomolecules, reaction and transition state elucidation, conformational flexibility determination, and drug design. Throughout, the chapters gradually build up from introductory level to comprehensive reviews of the latest research, and include all important compound classes, such as DNA, RNA, enzymes, vitamins, and heterocyclic compounds. The result is in-depth and vital knowledge for both readers already working in the field as well as those entering it. Includes contributions by Prof. Ada Yonath (Nobel Prize in Chemistry 2009) and Prof. Jerome Karle (Nobel Prize in Chemistry 1985).