LEADER 05603nam 2200697Ia 450 001 9910144110203321 005 20170809153208.0 010 $a1-281-84288-5 010 $a9786611842888 010 $a3-527-61567-9 010 $a3-527-61566-0 035 $a(CKB)1000000000552489 035 $a(EBL)481384 035 $a(OCoLC)289098579 035 $a(SSID)ssj0000097642 035 $a(PQKBManifestationID)11130869 035 $a(PQKBTitleCode)TC0000097642 035 $a(PQKBWorkID)10119879 035 $a(PQKB)11699598 035 $a(MiAaPQ)EBC481384 035 $a(EXLCZ)991000000000552489 100 $a19941020d1995 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvanced computer-assisted techniques in drug discovery$b[electronic resource] /$fedited by Han van de Waterbeemd 210 $aWeinheim ;$aNew York $cVCH$dc1995 215 $a1 online resource (367 p.) 225 1 $aMethods and principles in medicinal chemistry ;$vv. 3 300 $aDescription based upon print version of record. 311 $a3-527-29248-9 320 $aIncludes bibliographical references and index. 327 $aAdvanced Computer- Assisted Techniques in Drug Discovery; Preface; A Personal Foreword; Contents; 1 Introduction; 1.1 3D QSAR; 1.2 Databases; 1.3 Progress in Multivariate Data Analysis; 1.4 Scope of this Book; References; 2 3D QSAR: The Integration of QSAR with Molecular Modeling; 2.1 Chemometrics and Molecular Modeling; 2.1.1 Introduction; 2.1.2 QSAR Methodology using Molecular Modeling and Chemometrics; 2.1.2.1 Search for the Geometric Pharmacophore; 2.1.2.2 Quantitative Correlation between Molecular Properties and Activity; 2.1.2.3 Computer Programs; 2.1.3 Illustrative Examples 327 $a2.1.3.1 Amnesia-Reversal Compounds2.1.3.2 Non-Peptide Angiotensin II Receptor Antagonists; 2.1.3.3 HMG-CoA Reductase Inhibitors; 2.1.3.4 Antagonists at the 5-HT3 Receptor; 2.1.3.5 Polychlorinated Dibenzo-p-dioxins; 2.1.4 Conclusions; References; 2.2 3D QSAR Methods; 2.2.1 Introduction; 2.2.2 3D QSAR of a Series of Calcium Channel Agonists; 2.2.2.1 Molecular Alignment; 2.2.2.2 Charges; 2.2.2.3 Generating 3D Fields; 2.2.2.4 Compilation of GRID Maps; 2.2.2.5 Inclusion of Macroscopic Descriptors with 3D Field Data; 2.2.3 Statistical Analysis; 2.2.3.1 Results of the Analysis 327 $a2.2.3.2 Testing the Model2.2.4 Conclusions; References; 2.3 GOLPE Philosophy and Applications in 3D QSAR; 2.3.1 Introduction; 2.3.1.1 3D Molecular Descriptors and Chemometric Tools; 2.3.1.2 Unfolding Three-way Matrices; 2.3.2 The GOLPE Philosophy; 2.3.2.1 Variable Selection; 2.3.3 Applications; 2.3.3.1 PCA on the Target Matrix; 2.3.3.2 PCA on the Probe Matrix; 2.3.3.3 PLS Analysis on the Target Matrix; 2.3.3.4 PLS on Target Matrix as a Strategy to Ascertain the Active Conformation; 2.3.3.5 GOLPE with Different 3D Descriptors; 2.3.4 Conclusions and Perspectives; References 327 $a3 Rational Use of Chemical and Sequence Databases3.1 Molecular Similarity Analysis: Applications in Drug Discovery; 3.1.1 Introduction; 3.1.2 Similarity-Based Compound Selection; 3.1.2.1 Similarity Measures and Neighborhoods; 3.1.2.2 Application of 2D and 3D Similarity Measures; 3.1.2.3 Application of Dissimilarity-Based Compound Selection for Broad Screening; 3.1.3 Structure-Activity Maps (SAMs); 3.1.3.1 A Visual Analogy; 3.1.3.2 Representing Inter-Structure Distances; 3.1.3.3 Structure Maps; 3.1.3.4 Coloring a Structure Map; 3.1.4 Field-Based Similarity Methods 327 $a3.1.4.1 Field-Based Similarity Measures3.1.4.2 Field-Based Molecular Superpositions; 3.1.4.3 An Example of Field-Based Fitting: Morphine and Clonidine; 3.1.5 Conclusions; References; 3.2 Clustering of Chemical Structure Databases for Compound Selection; 3.2.1 Introduction; 3.2.2 Review of Clustering Methods; 3.2.2.1 Hierarchical Clustering Methods; 3.2.2.2 Non-Hierarchical Clustering Methods; 3.2.3 Choice of Clustering Method; 3.2.3.1 Computational Requirements; 3.2.3.2 Cluster Shapes; 3.2.3.3 Comparative Studies 327 $a3.2.4 Examples of the Selection of Compounds from Databases by Clustering Techniques 330 $aThe use of powerful computers has revolutionized molecular design and drug discovery. Thoroughly researched and well-structured, this comprehensive handbook covers highly effective and efficient techniques in 3D-QSAR and advanced statistical analysis. The emphasis is on showing users how to apply these methods and avoid costly and time-consuming methodical errors.Topics covered include* combination of statistical methods and molecular modeling tools* rational use of databases* advanced statistical techniques* neural networks and expert systems in molecular design