LEADER 05567nam 2200661Ia 450 001 9910144126803321 005 20170809175401.0 010 $a1-283-37033-6 010 $a9786613370334 010 $a3-527-61316-1 010 $a3-527-61317-X 035 $a(CKB)1000000000554890 035 $a(EBL)481387 035 $a(OCoLC)310354009 035 $a(SSID)ssj0000151511 035 $a(PQKBManifestationID)11161090 035 $a(PQKBTitleCode)TC0000151511 035 $a(PQKBWorkID)10317628 035 $a(PQKB)10711848 035 $a(MiAaPQ)EBC481387 035 $a(EXLCZ)991000000000554890 100 $a20011030d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aEvolutionary algorithms in molecular design$b[electronic resource] /$fedited by David E. Clark 210 $aWeinheim ;$aNew York $cWiley-VCH$dc2000 215 $a1 online resource (294 p.) 225 1 $aMethods and principles in medicinal chemistry ;$vv. 8 300 $aDescription based upon print version of record. 311 $a3-527-30155-0 320 $aIncludes bibliographical references and index. 327 $aEvolutionary Algorithms in Molecular Design; Contents; 1 Introduction to Evolutionary Algorithms; 1.1 History and Biological Motivation; 1.2 Descriptive Comparison of Algorithms; 1.2.1 Representation; 1.2.2 Evolotionary Operators; 1.2.3 Selection and the Next Generation; 1.2.4 Self-Adaptation and Learning-Rule Methods; 1.3 Implementation Issues and Representative Applications of EAs in Drug Design; 1.3.1 Problem-Adapted EA Features; 1.3.2 Problem Suitability for EA Implementation; 1.3.3 EA Combination Methods; 1.4 Conclusions; 2 Small-molecule Geometry Optimization and Conformational Search 327 $a2.1 Introduction2.2 Evolutionary Algorithms; 2.2.1 Diversity; 2.2.2 Creation of New Solutions; 2.2.3 Constraint Satisfaction; 2.3 Technical Aspects of Method Comparisons; 2.4 Traditional Methods for Structure Optimization; 2.5 Evolutionary Methods for Structure Optimization; 2.5.1 Satisfying Constraints from Experiments; 2.5.2 Energy Minimization; 2.6 Discussion; 2.7 Conclusions; 3 Protein-Ligand Docking; 3.1 Molecular Structure and Medicine; 3.2 Computational Protein-Ligand Docking; 3.2.1 Scoring Functions; 3.2.2 Level of Allowed Molecular Flexibility 327 $a3.2.3 Testing and Evaluating Docking Methods3.3 Evolutionary Algorithms for Protein-Ligand Docking; 3.4 Published Methods; 3.5 Representation of the Genome; 3.6 Hybrid Evolutionary Algorithms; 3.7 Conclusions; 4 De Now Molecular Design; 4.1 Introduction; 4.2 Overview of a Genetic Algorithm; 4.3 Defining the Constraints; 4.4 Applications of EAs to De Novo Design; 4.5 Applications of EAs to Pharmacophore Mapping; 4.6 Applications of EAs to Receptor Modeling; 4.7 Discussion; 4.8 Conclusions; 5 Quantitative Structure- Activity Relationships; 5.1 Introduction; 5.2 Key Tasks in QSAR Development 327 $a5.2.1 Descriptor Tabulation5.2.2 Feature Selection; 5.2.3 Model Construction; 5.2.4 Model Validation; 5.3 Availability of GA Programs; 5.4 Applications of GAs in QSAR; 5.4.1 GA-MLR Approach; 5.4.2 GA-PLS; 5.4.3 GA-NN; 5.4.4 Chance Correlation; 5.5 Discussion; 6 Chemometrics; 6.1 Introduction; 6.2 Parameter Estimation; 6.2.1 Curve Fitting; 6.2.2 Nonlinear Modeling; 6.2.3 Neural Networks; 6.3 Subset Selection; 6.3.1 Feature Selection; 6.3.2 Object Selection; 6.4 Miscellaneous; 6.4.1 Clustering and Classification; 6.5 Discussion; 7 Chemical Structure Handling; 7.1 Introduction 327 $a7.2 Representation and Searching of Chemical Structures7.3 Processing of 2-D Chemical Graphs; 7.4 Processing of 3-D Chemical Graphs; 7.4.1 Flexible 3-D Substructure Searching; 7.4.2 Identification of Common Structural Features in Sets of Ligands; 7.5 Field-Based Similarity Searching; 7.6 Generation of Molecular Alignments; 7.7 Conclusions; 8 Molecular Diversity Analysis and Combmatorial Library Design; 8.1 Introduction; 8.2 The Diversity of Genotypes: The Space of Chemistry; 8.3 The Diversity of Phenotypes: The Property Space; 8.4 Diversity and Distance Calculation 327 $a8.5 Connecting the Structure and the Property Space: Evolutionary Algorithms 330 $aWhen trying to find new methods and problem-solving strategies for their research, scientists often turn to nature for inspiration. An excellent example of this is the application of Darwin's Theory of Evolution, particularly the notion of the 'survival of the fittest', in computer programs designed to search for optimal solutions to many kinds of problems. These 'evolutionary algorithms' start from a population of possible solutions to a given problem and, by applying evolutionary principles, evolve successive generations with improved characteristics until an optimal, or near-optimal, soluti 410 0$aMethods and principles in medicinal chemistry ;$vv. 8. 606 $aDrugs$xDesign$xMathematical models 606 $aEvolutionary computation 606 $aEvolutionary programming (Computer science) 608 $aElectronic books. 615 0$aDrugs$xDesign$xMathematical models. 615 0$aEvolutionary computation. 615 0$aEvolutionary programming (Computer science) 676 $a615.1900285 701 $aClark$b David E.$f1966-$0945456 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144126803321 996 $aEvolutionary algorithms in molecular design$92134479 997 $aUNINA