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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.
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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. Download and install galaxy s3 accuweather clock widget mods in various styles. 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.
The Advantages of Field-Based QSAR Quantitative structure-activity relationships (QSAR) have long been a favorite way for researchers to optimize lead compounds. However, traditional QSAR models typically incorporate only crude approximations of 3D structure. Field-Based QSAR disposes of this limitation. Beginning with a set of aligned ligands that have known activities, Field-Based QSAR is capable of inferring how the ligand’s electrostatic, hydrophobic, and steric fields result in biological activity or inactivity. Like all ligand-based approaches, Field-Based QSAR requires no knowledge of receptor structure. Ideal for both lead discovery and lead optimization, Field-Based QSAR is capable of quickly turning existing data sets into useful QSAR models, helping researchers to leapfrog around patent space, synthetic roadblocks, and ADME restrictions.
Exercise fine-grained control over model-building: Field-Based QSAR gives users a fine degree of control over model building. Working from the graphical interface, researchers can adjust the most important model-building parameters. Specialists will find a plethora of additional options available to them on the command line. Look beyond R2 values: Field-Based QSAR makes it easy to validate the predictive power of your model.
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