Significant progress has been made in the study of three-dimensional quantitative structure-activity relationships (3D QSAR) since the first publication by Richard Cramer in 1988 and the first volume in the series, 3D QSAR in Drug Design. Theory, Methods and Applications, published in 1993. The aim of that early book was to contribute to the understanding and the further application of CoMFA and related approaches and to facilitate the appropriate use of these methods. Since then, hundreds of papers have appeared using the quickly developing techniques of both 3D QSAR and computational sciences to study a broad variety of biological problems. Again the editor(s) felt that the time had come to solicit reviews on published and new viewpoints to document the state of the art of 3D QSAR in its broadest definition and to provide visions of where new techniques will emerge or new appli- tions may be found. The intention is not only to highlight new ideas but also to show the shortcomings, inaccuracies, and abuses of the methods. We hope this book will enable others to separate trivial from visionary approaches and me-too methodology from in- vative techniques. These concerns guided our choice of contributors. To our delight, our call for papers elicited a great many manuscripts.

Since the publication of the first edition, the field has changed dramatically. Scientists can now explicitly consider 3D features in quantitative structure-activity relationship (QSAR) studies and often have the 3D structure of the macromolecular target to guide the 3D QSAR. Improvements in computer hardware and software have also made the methods more accessible to scientists. Taking these developments into account, Quantitative Drug Design: A Critical Introduction, Second Edition shows scientists how to apply QSAR techniques at a state-of-the-art level. New to the Second Edition A new chapter on methods that identify the 3D conformations to use for 3D QSAR New discussions on partial least squares, multidimensional scaling, clustering, support vector machines, kNN potency prediction, and recursive partitioning Expanded case studies that include the results of data that has been re-analyzed using newer methods A new case study on the discovery of novel dopaminergics with pharmacophore mapping and CoMFA A new case study on the application of CoMFA to series in which the 3D structure of the ligand-protein complex is known Based on the author's four decades of experience in all areas of ligand-based computer-assisted drug design, this invaluable book describes how to transform ligand structure-activity relationships into models that predict the potency or activity/inactivity of new molecules. It will help you avoid traps when dealing with quantitative drug design.

Since the publication of the first edition, the field has changed dramatically. Scientists can now explicitly consider 3D features in quantitative structure-activity relationship (QSAR) studies and often have the 3D structure of the macromolecular target to guide the 3D QSAR. Improvements in computer hardware and software have also made the methods more accessible to scientists. Taking these developments into account, Quantitative Drug Design A Critical Introduction, Second Edition shows scientists how to apply QSAR techniques at a state-of-the-art level.

New to the Second Edition

• A new chapter on methods that identify the 3D conformations to use for 3D QSAR
• New discussions on partial least squares, multidimensional scaling, clustering, support vector machines, kNN potency prediction, and recursive partitioning
• Expanded case studies that include the results of data that has been re-analyzed using newer methods
• A new case study on the discovery of novel dopaminergics with pharmacophore mapping and CoMFA
• A new case study on the application of CoMFA to series in which the 3D structure of the ligand-protein complex is known

Based on the author s four decades of experience in all areas of ligand-based computer-assisted drug design, this invaluable book describes how to transform ligand structure-activity relationships into models that predict the potency or activity/inactivity of new molecules. It will help you avoid traps when dealing with quantitative drug design.