3D QSAR in Drug Design: Volume 1: Theory Methods and ApplicationsHugo Kubinyi 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. |
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Contents
An Introduction | 3 |
DrugReceptor Interactions | 13 |
Generation and Use in 3D Searching | 41 |
BindingSite Modeling of Unknown Receptors | 80 |
Pharmacophore Identification | 117 |
ComputerAssisted Pharmacophore Identification | 137 |
Molecular Similarity | 150 |
Structural Alignment of Molecules | 173 |
ThreeDimensional Receptor Modelling Using Distance Geometry and Voronoi Polyhedra | 407 |
The Hypothetical ActiveSite Lattice | 429 |
The Developing Practice of Comparative Molecular Field Analysis | 441 |
Molecular Interaction Fields | 484 |
Hydrophobic Fields | 504 |
PLS Partial LeastSquares Projections to Latent Structures | 521 |
Variable Selection in PLS Analysis | 549 |
Series Design | 565 |
Molecular Superposition for Rational Drug Design | 200 |
Multiple Binding Modes | 226 |
QSAR and Molecular Graphics | 257 |
Advances in Molecular Shape Analysis | 276 |
MTD and Hyperstructure Approaches | 305 |
Binding Site Models | 318 |
Minireceptors and Pseudoreceptors | 334 |
Molecular Modelling of G ProteinCoupled Receptors | 353 |
Steroid Receptor Models | 371 |
Ligand Design | 384 |
Scope and Limitations | 581 |
Comparison of Classical and 3D QSAR | 617 |
A Case Study | 641 |
Applications of CoMFA and Related 3D QSAR Approaches | 659 |
695 | |
Recommendations for CoMFA Studies and 3D QSAR Publications | 709 |
Practical Problems in PLS Analyses | 715 |
Author Index | 729 |
731 | |
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3D QSAR in Drug Design: Volume 1: Theory Methods and Applications Hugo Kubinyi No preview available - 2011 |
Common terms and phrases
3D searching 3D structures affinity agonists algorithm alignment amino acid analogs approach binding energy binding modes binding sites biological activity calculated Chem chemical coefficients CoMFA CoMFA model Comparative Molecular Field complex components compounds Comput Comput.-Aided Mol conformational correlation corresponding cross-validation crystal structure data set database derived descriptors dihydrofolate reductase distance geometry drug design electronic electrostatic fields electrostatic potential entropy enzyme experimental flexible force field free energy functional groups graphics grid Hansch HASL hydrogen bonds hydrophobic inhibitors interaction energies lattice ligand lipophilicity maps matrix method Molecular Field Analysis molecular modeling molecular shape molecules optimal parameters peptide pharmacophore PLS analysis points position predictive problem procedure properties protein QSAR studies quantitative structure-activity relationships region residues ring rotational selection side chain space steric steroid Struct.-Act structure-activity relationships subsite substituents superposed superposition surface Table tion torsional training set values variables variance Vectors Waals