{"product_id":"tutorials-in-chemoinformatics-9781119137962","title":"Tutorials in Chemoinformatics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets    Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Contributors xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xix\u003ci\u003e            \u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Chemical Databases 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Curation 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGilles Marcou and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 3\u003c\/p\u003e \u003cp\u003eSoftware 5\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 7\u003c\/p\u003e \u003cp\u003eConclusion 34\u003c\/p\u003e \u003cp\u003eReferences 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Relational Chemical Databases: Creation, Management, and Usage 37\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGilles Marcou and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 37\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 41\u003c\/p\u003e \u003cp\u003eConclusion 65\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Handling of Markush Structures 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTimur Madzhidov, Ramil Nugmanov, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 67\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 68\u003c\/p\u003e \u003cp\u003eConclusion 73\u003c\/p\u003e \u003cp\u003eReferences 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Processing of SMILES, InChI, and Hashed Fingerprints 75\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJoão Montargil Aires de Sousa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 75\u003c\/p\u003e \u003cp\u003eAlgorithms 76\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 78\u003c\/p\u003e \u003cp\u003eConclusion 80\u003c\/p\u003e \u003cp\u003eReferences 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Library Design 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Design of Diverse and Focused Compound Libraries 85\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAntonio de la Vega de Leon, Eugen Lounkine, Martin Vogt, and Jürgen Bajorath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 85\u003c\/p\u003e \u003cp\u003eData Acquisition 86\u003c\/p\u003e \u003cp\u003eImplementation 86\u003c\/p\u003e \u003cp\u003eCompound Library Creation 87\u003c\/p\u003e \u003cp\u003eCompound Library Analysis 90\u003c\/p\u003e \u003cp\u003eNormalization of Descriptor Values 91\u003c\/p\u003e \u003cp\u003eVisualizing Descriptor Distributions 92\u003c\/p\u003e \u003cp\u003eDecorrelation and Dimension Reduction 94\u003c\/p\u003e \u003cp\u003ePartitioning and Diverse Subset Calculation 95\u003c\/p\u003e \u003cp\u003ePartitioning 95\u003c\/p\u003e \u003cp\u003eDiverse Subset Selection 97\u003c\/p\u003e \u003cp\u003eCombinatorial Libraries 98\u003c\/p\u003e \u003cp\u003eCombinatorial Enumeration of Compounds 98\u003c\/p\u003e \u003cp\u003eRetrosynthetic Approaches to Library Design 99\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3 Data Analysis and Visualization 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Hierarchical Clustering in R 105\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMartin Vogt and Jürgen Bajorath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 105\u003c\/p\u003e \u003cp\u003eAlgorithms 106\u003c\/p\u003e \u003cp\u003eInstructions 107\u003c\/p\u003e \u003cp\u003eHierarchical Clustering Using Fingerprints 108\u003c\/p\u003e \u003cp\u003eHierarchical Clustering Using Descriptors 111\u003c\/p\u003e \u003cp\u003eVisualization of the Data Sets 113\u003c\/p\u003e \u003cp\u003eAlternative Clustering Methods 116\u003c\/p\u003e \u003cp\u003eConclusion 117\u003c\/p\u003e \u003cp\u003eReferences 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Data Visualization and Analysis Using Kohonen Self‐Organizing Maps 119\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJoão Montargil Aires de Sousa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 119\u003c\/p\u003e \u003cp\u003eAlgorithms 120\u003c\/p\u003e \u003cp\u003eInstructions 121\u003c\/p\u003e \u003cp\u003eConclusion 126\u003c\/p\u003e \u003cp\u003eReferences 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4 Obtaining and Validation QSAR\/QSPR Models 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Descriptors Generation Using the CDK Toolkit and Web Services 129\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJoão Montargil Aires de Sousa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 129\u003c\/p\u003e \u003cp\u003eAlgorithms 130\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 131\u003c\/p\u003e \u003cp\u003eConclusion 133\u003c\/p\u003e \u003cp\u003eReferences 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 QSPR Models on Fragment Descriptors 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVitaly Solov’ev and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 135\u003c\/p\u003e \u003cp\u003eData 136\u003c\/p\u003e \u003cp\u003eISIDA_QSPR Input 137\u003c\/p\u003e \u003cp\u003eData Split Into Training and Test Sets 139\u003c\/p\u003e \u003cp\u003eSubstructure Molecular Fragment (SMF) Descriptors 139\u003c\/p\u003e \u003cp\u003eRegression Equations 142\u003c\/p\u003e \u003cp\u003eForward and Backward Stepwise Variable Selection 142\u003c\/p\u003e \u003cp\u003eParameters of Internal Model Validation 143\u003c\/p\u003e \u003cp\u003eApplicability Domain (AD) of the Model 143\u003c\/p\u003e \u003cp\u003eStorage and Retrieval Modeling Results 144\u003c\/p\u003e \u003cp\u003eAnalysis of Modeling Results 144\u003c\/p\u003e \u003cp\u003eRoot‐Mean Squared Error (RMSE) Estimation 148\u003c\/p\u003e \u003cp\u003eSetting the Parameters 151\u003c\/p\u003e \u003cp\u003eAnalysis of n‐Fold Cross‐Validation Results 151\u003c\/p\u003e \u003cp\u003eLoading Structure‐Data File 153\u003c\/p\u003e \u003cp\u003eDescriptors and Fitting Equation 154\u003c\/p\u003e \u003cp\u003eVariables Selection 155\u003c\/p\u003e \u003cp\u003eConsensus Model 155\u003c\/p\u003e \u003cp\u003eModel Applicability Domain 155\u003c\/p\u003e \u003cp\u003en‐Fold External Cross‐Validation 155\u003c\/p\u003e \u003cp\u003eSaving and Loading of the Consensus Modeling Results 155\u003c\/p\u003e \u003cp\u003eStatistical Parameters of the Consensus Model 156\u003c\/p\u003e \u003cp\u003eConsensus Model Performance as a Function of Individual Models Acceptance Threshold 157\u003c\/p\u003e \u003cp\u003eBuilding Consensus Model on the Entire Data Set 158\u003c\/p\u003e \u003cp\u003eLoading Input Data 159\u003c\/p\u003e \u003cp\u003eLoading Selected Models and Choosing their Applicability Domain 160\u003c\/p\u003e \u003cp\u003eReporting Predicted Values 160\u003c\/p\u003e \u003cp\u003eAnalysis of the Fragments Contributions 161\u003c\/p\u003e \u003cp\u003eReferences 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Cross‐Validation and the Variable Selection Bias 163\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 163\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 165\u003c\/p\u003e \u003cp\u003eConclusion 172\u003c\/p\u003e \u003cp\u003eReferences 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Classification Models 175\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 176\u003c\/p\u003e \u003cp\u003eAlgorithms 178\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 180\u003c\/p\u003e \u003cp\u003eConclusion 191\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Regression Models 193\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 194\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 197\u003c\/p\u003e \u003cp\u003eConclusion 207\u003c\/p\u003e \u003cp\u003eReferences 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Benchmarking Machine‐Learning Methods 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 209\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 210\u003c\/p\u003e \u003cp\u003eConclusion 222\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Compound Classification Using the scikit‐learn Library 223\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJenny Balfer, Jürgen Bajorath, and Martin Vogt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 224\u003c\/p\u003e \u003cp\u003eAlgorithms 225\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 230\u003c\/p\u003e \u003cp\u003eNaïve Bayes 230\u003c\/p\u003e \u003cp\u003eDecision Tree 231\u003c\/p\u003e \u003cp\u003eSupport Vector Machine 234\u003c\/p\u003e \u003cp\u003eNotes on Provided Code 237\u003c\/p\u003e \u003cp\u003eConclusion 238\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5 Ensemble Modeling 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Bagging and Boosting of Classification Models 243\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 243\u003c\/p\u003e \u003cp\u003eAlgorithm 244\u003c\/p\u003e \u003cp\u003eStep by Step Instructions 245\u003c\/p\u003e \u003cp\u003eConclusion 247\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Bagging and Boosting of Regression Models 249\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 249\u003c\/p\u003e \u003cp\u003eAlgorithm 249\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 250\u003c\/p\u003e \u003cp\u003eConclusion 255\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Instability of Interpretable Rules 257\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 257\u003c\/p\u003e \u003cp\u003eAlgorithm 258\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 258\u003c\/p\u003e \u003cp\u003eConclusion 261\u003c\/p\u003e \u003cp\u003eReferences 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Random Subspaces and Random Forest 263\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 264\u003c\/p\u003e \u003cp\u003eAlgorithm 264\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 265\u003c\/p\u003e \u003cp\u003eConclusion 269\u003c\/p\u003e \u003cp\u003eReferences 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Stacking 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIgor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eTheoretical Background 271\u003c\/p\u003e \u003cp\u003eAlgorithm 272\u003c\/p\u003e \u003cp\u003eStep‐by‐Step Instructions 273\u003c\/p\u003e \u003cp\u003eConclusion 277\u003c\/p\u003e \u003cp\u003eReferences 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6 3D Pharmacophore Modeling 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 3D Pharmacophore Modeling Techniques in Computer‐Aided Molecular Design Using LigandScout 281\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThomas Seidel, Sharon D. Bryant, Gökhan Ibis, Giulio Poli, and Thierry Langer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 281\u003c\/p\u003e \u003cp\u003eTheory: 3D Pharmacophores 283\u003c\/p\u003e \u003cp\u003eRepresentation of Pharmacophore Models 283\u003c\/p\u003e \u003cp\u003eHydrogen‐Bonding Interactions 285\u003c\/p\u003e \u003cp\u003eHydrophobic Interactions 285\u003c\/p\u003e \u003cp\u003eAromatic and Cation‐π Interactions 286\u003c\/p\u003e \u003cp\u003eIonic Interactions 286\u003c\/p\u003e \u003cp\u003eMetal Complexation 286\u003c\/p\u003e \u003cp\u003eLigand Shape Constraints 287\u003c\/p\u003e \u003cp\u003ePharmacophore Modeling 288\u003c\/p\u003e \u003cp\u003eManual Pharmacophore Construction 288\u003c\/p\u003e \u003cp\u003eStructure‐Based Pharmacophore Models 289\u003c\/p\u003e \u003cp\u003eLigand‐Based Pharmacophore Models 289\u003c\/p\u003e \u003cp\u003e3D Pharmacophore‐Based Virtual Screening 291\u003c\/p\u003e \u003cp\u003e3D Pharmacophore Creation 291\u003c\/p\u003e \u003cp\u003eAnnotated Database Creation 291\u003c\/p\u003e \u003cp\u003eVirtual Screening‐Database Searching 292\u003c\/p\u003e \u003cp\u003eHit‐List Analysis 292\u003c\/p\u003e \u003cp\u003eTutorial: Creating 3D‐Pharmacophore Models Using LigandScout 294\u003c\/p\u003e \u003cp\u003eCreating Structure‐Based Pharmacophores From a Ligand‐Protein Complex 294\u003c\/p\u003e \u003cp\u003eDescription: Create a Structure‐Based Pharmacophore Model 296\u003c\/p\u003e \u003cp\u003eCreate a Shared Feature Pharmacophore Model From Multiple Ligand‐Protein Complexes 296\u003c\/p\u003e \u003cp\u003eDescription: Create a Shared Feature Pharmacophore and Align it to Ligands 297\u003c\/p\u003e \u003cp\u003eCreate Ligand‐Based Pharmacophore Models 298\u003c\/p\u003e \u003cp\u003eDescription: Ligand‐Based Pharmacophore Model Creation 300\u003c\/p\u003e \u003cp\u003eTutorial: Pharmacophore‐Based Virtual Screening Using LigandScout 301\u003c\/p\u003e \u003cp\u003eVirtual Screening, Model Editing, and Viewing Hits in the Target Active Site 301\u003c\/p\u003e \u003cp\u003eDescription: Virtual Screening and Pharmacophore Model Editing 302\u003c\/p\u003e \u003cp\u003eAnalyzing Screening Results with Respect to the Binding Site 303\u003c\/p\u003e \u003cp\u003eDescription: Analyzing Hits in the Active Site Using LigandScout 305\u003c\/p\u003e \u003cp\u003eParallel Virtual Screening of Multiple Databases Using LigandScout 305\u003c\/p\u003e \u003cp\u003eVirtual Screening in the Screening Perspective of LigandScout 306\u003c\/p\u003e \u003cp\u003eDescription: Virtual Screening Using LigandScout 306\u003c\/p\u003e \u003cp\u003eConclusions 307\u003c\/p\u003e \u003cp\u003eAcknowledgments 307\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 7 The Protein 3D‐Structures in Virtual Screening 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 The Protein 3D‐Structures in Virtual Screening 313\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eInna Slynko and Esther Kellenberger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 313\u003c\/p\u003e \u003cp\u003eDescription of the Example Case 314\u003c\/p\u003e \u003cp\u003eThrombin and Blood Coagulation 314\u003c\/p\u003e \u003cp\u003eActive Thrombin and Inactive Prothrombin 314\u003c\/p\u003e \u003cp\u003eThrombin as a Drug Target 314\u003c\/p\u003e \u003cp\u003eThrombin Three‐Dimensional Structure: The 1OYT PDB File 315\u003c\/p\u003e \u003cp\u003eModeling Suite 315\u003c\/p\u003e \u003cp\u003eOverall Description of the Input Data Available on the Editor Website 315\u003c\/p\u003e \u003cp\u003eExercise 1: Protein Analysis and Preparation 316\u003c\/p\u003e \u003cp\u003eStep 1: Identification of Molecules Described in the 1OYT PDB File 316\u003c\/p\u003e \u003cp\u003eStep 2: Protein Quality Analysis of the Thrombin\/Inhibitor PDB Complex Using MOE Geometry Utility 320\u003c\/p\u003e \u003cp\u003eStep 3: Preparation of the Protein for Drug Design Applications 321\u003c\/p\u003e \u003cp\u003eStep 4: Description of the Protein‐Ligand Binding Mode 325\u003c\/p\u003e \u003cp\u003eStep 5: Detection of Protein Cavities 328\u003c\/p\u003e \u003cp\u003eExercise 2: Retrospective Virtual Screening Using the Pharmacophore Approach 330\u003c\/p\u003e \u003cp\u003eStep 1: Description of the Test Library 332\u003c\/p\u003e \u003cp\u003eStep 2.1: Pharmacophore Design, Overview 333\u003c\/p\u003e \u003cp\u003eStep 2.2: Pharmacophore Design, Flexible Alignment of Three Thrombin Inhibitors 334\u003c\/p\u003e \u003cp\u003eStep 2.3: Pharmacophore Design, Query Generation 335\u003c\/p\u003e \u003cp\u003eStep 3: Pharmacophore Search 337\u003c\/p\u003e \u003cp\u003eExercise 3: Retrospective Virtual Screening Using the Docking Approach 341\u003c\/p\u003e \u003cp\u003eStep 1: Description of the Test Library 341\u003c\/p\u003e \u003cp\u003eStep 2: Preparation of the Input 341\u003c\/p\u003e \u003cp\u003eStep 3: Re‐Docking of the Crystallographic Ligand 341\u003c\/p\u003e \u003cp\u003eStep 4: Virtual Screening of a Database 345\u003c\/p\u003e \u003cp\u003eGeneral Conclusion 350\u003c\/p\u003e \u003cp\u003eReferences 351\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 8 Protein‐Ligand Docking 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Protein‐Ligand Docking 355\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eInna Slynko, Didier Rognan, and Esther Kellenberger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 355\u003c\/p\u003e \u003cp\u003eDescription of the Example Case 356\u003c\/p\u003e \u003cp\u003eMethods 356\u003c\/p\u003e \u003cp\u003eLigand Preparation 359\u003c\/p\u003e \u003cp\u003eProtein Preparation 359\u003c\/p\u003e \u003cp\u003eDocking Parameters 360\u003c\/p\u003e \u003cp\u003eDescription of Input Data Available on the Editor Website 360\u003c\/p\u003e \u003cp\u003eExercises 362\u003c\/p\u003e \u003cp\u003eA Quick Start with LeadIT 362\u003c\/p\u003e \u003cp\u003eRe‐Docking of Tacrine into AChE 362\u003c\/p\u003e \u003cp\u003ePreparation of AChE From 1ACJ PDB File 362\u003c\/p\u003e \u003cp\u003eDocking of Neutral Tacrine, then of Positively Charged Tacrine 363\u003c\/p\u003e \u003cp\u003eDocking of Positively Charged Tacrine in AChE in Presence of Water 365\u003c\/p\u003e \u003cp\u003eCross‐Docking of Tacrine‐Pyridone and Donepezil Into AChE 366\u003c\/p\u003e \u003cp\u003ePreparation of AChE From 1ACJ PDB File 366\u003c\/p\u003e \u003cp\u003eCross‐Docking of Tacrine‐Pyridone Inhibitor and Donepezil in AChE in Presence of Water 367\u003c\/p\u003e \u003cp\u003eRe‐Docking of Donepezil in AChE in Presence of Water 370\u003c\/p\u003e \u003cp\u003eGeneral Conclusions 372\u003c\/p\u003e \u003cp\u003eAnnex: Screen Captures of LeadIT Graphical Interface 372\u003c\/p\u003e \u003cp\u003eReferences 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 9 Pharmacophorical Profiling Using Shape Analysis 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Pharmacophorical Profiling Using Shape Analysis 379\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJérémy Desaphy, Guillaume Bret, Inna Slynko, Didier Rognan, and Esther Kellenberger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 379\u003c\/p\u003e \u003cp\u003eDescription of the Example Case 380\u003c\/p\u003e \u003cp\u003eAim and Context 380\u003c\/p\u003e \u003cp\u003eDescription of the Searched Data Set 381\u003c\/p\u003e \u003cp\u003eDescription of the Query 381\u003c\/p\u003e \u003cp\u003eMethods 381\u003c\/p\u003e \u003cp\u003eRocs 381\u003c\/p\u003e \u003cp\u003eVolSite and Shaper 384\u003c\/p\u003e \u003cp\u003eOther Programs for Shape Comparison 384\u003c\/p\u003e \u003cp\u003eDescription of Input Data Available on the Editor Website 385\u003c\/p\u003e \u003cp\u003eExercises 387\u003c\/p\u003e \u003cp\u003ePreamble: Practical Considerations 387\u003c\/p\u003e \u003cp\u003eLigand Shape Analysis 387\u003c\/p\u003e \u003cp\u003eWhat are ROCS Output Files? 387\u003c\/p\u003e \u003cp\u003eBinding Site Comparison 388\u003c\/p\u003e \u003cp\u003eConclusions 390\u003c\/p\u003e \u003cp\u003eReferences 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 10 Algorithmic Chemoinformatics 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Algorithmic Chemoinformatics 395\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMartin Vogt, Antonio de la Vega de Leon, and Jürgen Bajorath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 395\u003c\/p\u003e \u003cp\u003eSimilarity Searching Using Data Fusion Techniques 396\u003c\/p\u003e \u003cp\u003eIntroduction to Virtual Screening 396\u003c\/p\u003e \u003cp\u003eThe Three Pillars of Virtual Screening 397\u003c\/p\u003e \u003cp\u003eMolecular Representation 397\u003c\/p\u003e \u003cp\u003eSimilarity Function 397\u003c\/p\u003e \u003cp\u003eSearch Strategy (Data Fusion) 397\u003c\/p\u003e \u003cp\u003eFingerprints 397\u003c\/p\u003e \u003cp\u003eCount Fingerprints 397\u003c\/p\u003e \u003cp\u003eFingerprint Representations 399\u003c\/p\u003e \u003cp\u003eBit Strings 399\u003c\/p\u003e \u003cp\u003eFeature Lists 399\u003c\/p\u003e \u003cp\u003eGeneration of Fingerprints 399\u003c\/p\u003e \u003cp\u003eSimilarity Metrics 402\u003c\/p\u003e \u003cp\u003eSearch Strategy 404\u003c\/p\u003e \u003cp\u003eCompleted Virtual Screening Program 405\u003c\/p\u003e \u003cp\u003eBenchmarking VS Performance 406\u003c\/p\u003e \u003cp\u003eScoring the Scorers 407\u003c\/p\u003e \u003cp\u003eHow to Score 407\u003c\/p\u003e \u003cp\u003eMultiple Runs and Reproducibility 408\u003c\/p\u003e \u003cp\u003eAdjusting the VS Program for Benchmarking 408\u003c\/p\u003e \u003cp\u003eAnalyzing Benchmark Results 410\u003c\/p\u003e \u003cp\u003eConclusion 414\u003c\/p\u003e \u003cp\u003eIntroduction to Chemoinformatics Toolkits 415\u003c\/p\u003e \u003cp\u003eTheoretical Background 415\u003c\/p\u003e \u003cp\u003eA Note on Graph Theory 416\u003c\/p\u003e \u003cp\u003eBasic Usage: Creating and Manipulating Molecules in RDKit 417\u003c\/p\u003e \u003cp\u003eCreation of Molecule Objects 417\u003c\/p\u003e \u003cp\u003eMolecule Methods 418\u003c\/p\u003e \u003cp\u003eAtom Methods 418\u003c\/p\u003e \u003cp\u003eBond Methods 419\u003c\/p\u003e \u003cp\u003eAn Example: Hill Notation for Molecules 419\u003c\/p\u003e \u003cp\u003eCanonical SMILES: The Canon Algorithm 420\u003c\/p\u003e \u003cp\u003eTheoretical Background 420\u003c\/p\u003e \u003cp\u003eRecap of SMILES Notation 420\u003c\/p\u003e \u003cp\u003eCanonical SMILES 421\u003c\/p\u003e \u003cp\u003eBuilding a SMILES String 422\u003c\/p\u003e \u003cp\u003eCanonicalization of SMILES 425\u003c\/p\u003e \u003cp\u003eThe Initial Invariant 427\u003c\/p\u003e \u003cp\u003eThe Iteration Step 428\u003c\/p\u003e \u003cp\u003eSummary 431\u003c\/p\u003e \u003cp\u003eSubstructure Searching: The Ullmann Algorithm 432\u003c\/p\u003e \u003cp\u003eTheoretical Background 432\u003c\/p\u003e \u003cp\u003eBacktracking 433\u003c\/p\u003e \u003cp\u003eA Note on Atom Order 436\u003c\/p\u003e \u003cp\u003eThe Ullmann Algorithm 436\u003c\/p\u003e \u003cp\u003eSample Runs 440\u003c\/p\u003e \u003cp\u003eSummary 441\u003c\/p\u003e \u003cp\u003eAtom Environment Fingerprints 441\u003c\/p\u003e \u003cp\u003eTheoretical Background 441\u003c\/p\u003e \u003cp\u003eImplementation 443\u003c\/p\u003e \u003cp\u003eThe Hashing Function 443\u003c\/p\u003e \u003cp\u003eThe Initial Atom Invariant 444\u003c\/p\u003e \u003cp\u003eThe Algorithm 444\u003c\/p\u003e \u003cp\u003eSummary 447\u003c\/p\u003e \u003cp\u003eReferences 447\u003c\/p\u003e \u003cp\u003eIndex 449\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406995071319,"sku":"9781119137962","price":77.85,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119137962.jpg?v=1730497823","url":"https:\/\/bookcurl.com\/products\/tutorials-in-chemoinformatics-9781119137962","provider":"Book 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