As a result of new statistical and mathematical approaches,
improved visualization tools, and recognition by international
regulatory groups, quantitative structure-activity relationships
(QSARs) now play important roles in pharmacology for the design of
new drugs as well as in toxicology and ecotoxicology for hazard
identification and risk assessment. Providing up-to-date coverage
of the field, Three Dimensional QSAR: Applications in Pharmacology
and Toxicology presents the most recent QSAR methods and
illustrates their scope, advantages, and limitations. Part I The
first part of the book addresses CoMFA and related methods, such as
CoMSIA, FLUFF, SOMFA. It also describes shape-, surface-, and
volume-based approaches, including MSA, excluded volume, LIV, HASL,
receptor surface model, COMPASS, and CoMSA. Part II Focusing on
methods that use 3D information, the second part covers
autocorrelation methods, such as GRIND; similarity-based methods,
including similarity matrices and quantum similarity indices; and
quantitative spectroscopic data-activity relationships. Some
applications in data mining are also explored. Part III The third
part deals with post-3D models. The authors discuss the adaptation
of the receptor and simultaneous presence of several conformers or
solvation mechanisms. Part IV The final part presents
receptor-related approaches as well as docking and free energy
calculations, which are treated at various levels. This part
concerns the extensive sampling of phase space and approximate
methods, such as linear interaction energy, Poisson-Boltzmann, and
generalized Born models. A case study covering several parallel
approaches is also developed. An appendix offers the basic
principles of modeling and statistical tools routinely required in
QSAR methodologies, including optimization methods, molecular
mechanics and dynamics, multivariate analysis, nonlinear models,
and evolutionary techniques. It provides newcomers
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