LEADER 00924nam0-22002891i-450- 001 990006171560403321 005 19980601 035 $a000617156 035 $aFED01000617156 035 $a(Aleph)000617156FED01 035 $a000617156 100 $a19980601d1992----km-y0itay50------ba 105 $a--------00-yy 200 1 $a<>droit a l'information administrative aux Etas-Unis$edu modele americain au systeme francais de trasparence$fGuy Scoffoni. 210 $aParis$cEconomica$d1992 215 $aXII,, 379 p.$d24 cm 225 1 $aScience et droit administratif / collection dirigee par C. Debbasc$v 676 $a350.819 700 1$aScoffoni,$bGuy$0234255 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006171560403321 952 $aVI D 225$b24589*$fFGBC 959 $aFGBC 996 $aDroit a l'information administrative aux Etas-Unis$9647293 997 $aUNINA DB $aGIU01 LEADER 05673nam 2200685Ia 450 001 9910144110503321 005 20170809153227.0 010 $a1-282-01035-2 010 $a9786612010354 010 $a3-527-61308-0 010 $a3-527-61309-9 035 $a(CKB)1000000000552467 035 $a(EBL)481394 035 $a(OCoLC)310353990 035 $a(SSID)ssj0000267790 035 $a(PQKBManifestationID)11239598 035 $a(PQKBTitleCode)TC0000267790 035 $a(PQKBWorkID)10211956 035 $a(PQKB)10072221 035 $a(MiAaPQ)EBC481394 035 $a(EXLCZ)991000000000552467 100 $a20010103d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aVirtual screening for bioactive molecules$b[electronic resource] /$fedited by Hans-Joachim Bo?hm and Gisbert Schneider 210 $aWeinheim ;$aNew York $cWiley-VCH$dc2000 215 $a1 online resource (327 p.) 225 1 $aMethods and principles in medicinal chemistry ;$v10 300 $aDescription based upon print version of record. 311 $a3-527-30153-4 320 $aIncludes bibliographical references and index. 327 $aVirtual Screening for Bioactive Molecules; Preface; A Personal Foreword; Contents; List of Contributors; Prologue; 1 High-Throughput Screening and Virtual Screening: Entry Points to Drug Discovery; 1.1 Introduction; 1.2 Miniaturization and Detection Strategies; 1.2.1 Screening Plate Format and Fluidics; 1.2.2 Detection Strategies; 1.2.3 Cell-Based Reporter Gene Assays; 1.2.4 Fluorescence Correlation Spectroscopy .; 1.2.5 Microchip Fabrication; 1.2.6 Remarks and Summary; 1.3 Compound Libraries; 1.4 Multi-Dimensional Optimization: Qualifying HTS Lead Candidates; 1.5 Conclusions; References 327 $a2 Library Filtering Systems and Prediction of Drug-Like Properties2.1 Introduction; 2.2 Simple Counting Methods to Predict Drug-Likeness; 2.3 Functional Group Filters; 2.4 "Chemistry Space" Methods; 2.5 Examination of Building Blocks in Known Drugs; 2.6 Other Methods; 2.7 Conclusions and Future Directions; References; 3 Prediction of Physicochemical Properties; 3.1 Introduction; 3.2 Prediction of Lipophilicity; 3.2.1 Fragment-Based Methods; 3.2.2 Methods Based on Molecular Properties; 3.2.3 Predictive Ability of Existing Techniques; 3.2.4 Other Solvent Systems; 3.2.5 Effect of Ionization 327 $a3.3 Prediction of Solubility3.3.1 Fragmental Approaches; 3.3.2 Property-Based Methods; 3.3.3 Conclusions; 3.4 Prediction of pKa; 3.4.1 Fragment-Based Methods; 3.4.2 Methods Based on Molecular Properties; 3.4.3 Conclusions; 3.5 Prediction of Protein Binding; 3.6 Conclusions; References; 4 Descriptor-Based Similarity Measures for Screening Chemical Databases; 4.1 Introduction; 4.2 Fragment-Based Similarity Searching; 4.3 Association and Distance Coefficients for Similarity Searching; 4.4 Structural Representations for Similarity Searching; 4.4.1 Descriptor Selection; 4.4.2 Descriptor Encoding 327 $a4.5 ConclusionsReferences; 5 Modelling Structure-Activity Relationships; 5.1 Introduction; 5.2 Hansch Analysis; 5.3 3-D QSAR; 5.4 Alignment-Free 3-D Descriptors; 5.5 Topological Descriptors; 5.6 Pharmacophores and Pharmacophoric Keys; 5.7 Conclusions; 5.8 Appendix - Statistical Techniques in QSAR and Pattern Recognition; 5.8.1 Data Reduction and Display; 5.8.1.1 Principal Component Analysis; 5.8.1.2 Non-Linear Mapping; 5.8.1.3 Neural Networks; 5.8.2 Regression Techniques; 5.8.2.1 Multiple Linear Regression; 5.8.2.2 Principal Component Regression and Partial Least Squares 327 $a5.8.3 Classification Techniques5.8.3.1 Linear Discriminant Analysis; 5.8.3.2 Soft Independent Modelling of Class Analogy; 5.8.3.3 Recursive Partitioning; References; 6 Database Profiling by Neural Networks; 6.1 "Drug-Likeness": A General Compound Property?; 6.2 Methods and Programs; 6.2.1 Databases; 6.2.2 Descriptors; 6.2.3 Classification Tools; 6.2.4 Complete Algorithm; 6.3 Applications; 6.3.1 Drug-Likeness and a Recipe for a Computational Filter; 6.3.2 Crop Protection Compounds; 6.3.3 Virtual High-Throughput Screens; 6.3.4 Optimization of Combinatorial Libraries; 6.4 Conclusions; References 327 $a7 Pharmacophore Pattern Application in Virtual Screening. Library Design and QSAR 330 $aRecent progress in high-throughput screening, combinatorial chemistry and molecular biology has radically changed the approach to drug discovery in the pharmaceutical industry. New challenges in synthesis result in new analytical methods. At present, typically 100,000 to one million molecules have to be tested within a short period and, therefore, highly effective screening methods are necessary for today's researchers - preparing and characterizing one compound after another belongs to the past. 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