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PART I: Perspectives on Virtual Screening |
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1 | (86) |
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Virtual Screening: Scope and Limitations |
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3 | (1) |
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Strategies to Virtual Screening |
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4 | (1) |
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Development of a Reliable Pharmacophore Hypothesis |
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5 | (1) |
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Series of Consecutive Hierarchical Filters to Match with the Pharmacophore Hypothesis |
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6 | (2) |
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Docking, Scoring, and Visual Inspection |
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8 | (1) |
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Virtual Screening: Matured as a Routine Tool? |
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9 | (1) |
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Peptide Bond Flip and Interstitial Water Molecules |
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10 | (2) |
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Changes of Protonation States Induced upon Ligand Binding |
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12 | (4) |
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Water, the Nasty, Frequently Ignored Binding Factor |
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16 | (1) |
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Protein Plasticity or ``How to Hit a Mobile Target?'' |
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17 | (2) |
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19 | (6) |
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19 | (1) |
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20 | (5) |
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Addressing the Virtual Screening Challenge: The Flex Approach |
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25 | (1) |
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Elementary Models for Docking and Structural Alignment Calculations |
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26 | (4) |
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26 | (1) |
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27 | (2) |
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29 | (1) |
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Basic Algorithmic Concepts |
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30 | (2) |
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Placing Molecular Fragments |
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30 | (1) |
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The Incremental Construction Phase |
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31 | (1) |
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Bringing It All Together: Base Selection---Base Placement---Incremental Construction -- Postoptimization |
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31 | (1) |
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Advanced Concepts for VS Tools |
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32 | (3) |
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32 | (1) |
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Docking under Pharmacophore Constraints |
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33 | (1) |
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33 | (1) |
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34 | (1) |
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Details to Be Taken into Account |
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34 | (1) |
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34 | (1) |
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34 | (1) |
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35 | (1) |
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35 | (1) |
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Supporting the Workflow---From an Algorithmic Engine to a VS Machine |
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35 | (2) |
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Scripting the Screening Process |
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35 | (1) |
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36 | (1) |
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36 | (1) |
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36 | (1) |
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A Practical Example---VS on a Cyclin-Dependent Kinase Target |
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37 | (10) |
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An Initial Test---Reproducing and Cross-Docking Crystal Structures |
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37 | (2) |
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39 | (1) |
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40 | (1) |
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Alignment-Based Screening for CDK2 Inhibitors |
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41 | (1) |
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42 | (1) |
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43 | (1) |
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43 | (4) |
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An Analysis of Critical Factors Affecting Docking and Scoring |
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47 | (2) |
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The Importance of Test Set Selection |
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49 | (5) |
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50 | (1) |
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Composition of the Test Set |
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51 | (1) |
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51 | (3) |
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Evaluation of Highly Regarded Docking Programs |
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54 | (10) |
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55 | (1) |
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56 | (1) |
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Gold (Cambridge Crystallographic Data Centre) |
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57 | (1) |
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57 | (6) |
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63 | (1) |
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Scoring Function Analysis |
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64 | (9) |
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Piecewise Linear Potential (PLP) |
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64 | (1) |
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65 | (1) |
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65 | (1) |
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Potential of Mean Force (PMF) |
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65 | (1) |
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65 | (1) |
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66 | (1) |
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66 | (1) |
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Correlations between Scoring Functions |
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66 | (1) |
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Scoring Function Accuracy |
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67 | (1) |
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The Effects of Training Set Selection |
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68 | (4) |
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72 | (1) |
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Evaluation of Docking/Scoring Combinations for Virtual Screening |
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73 | (6) |
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75 | (3) |
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78 | (1) |
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Overall Conclusions and Perspectives |
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79 | (8) |
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81 | (6) |
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PART II: Compound and Hit Suitability for Virtual Screening |
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87 | (38) |
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Compound Selection for Virtual Screening |
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The Role of Leads in Drug Discovery |
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89 | (3) |
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92 | (5) |
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Compound Processing Prior to Virtual Screening |
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97 | (6) |
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Assembling the Virtual Compound Collection |
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97 | (1) |
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98 | (4) |
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102 | (1) |
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Similarity Search if Known Active Molecules Are Available |
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103 | (1) |
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103 | (1) |
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103 | (4) |
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104 | (1) |
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104 | (1) |
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104 | (3) |
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Experimental Identification of Promiscuous, Aggregate-Forming Screening Hits |
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107 | (1) |
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108 | (2) |
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110 | (1) |
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111 | (11) |
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112 | (1) |
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113 | (2) |
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Inhibition of Dissimilar Enzymes |
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115 | (1) |
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Sensitivity to Enzyme Concentration |
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116 | (1) |
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117 | (1) |
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118 | (1) |
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119 | (1) |
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119 | (1) |
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119 | (1) |
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120 | (1) |
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120 | (2) |
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Problems and Troubleshooting |
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122 | (1) |
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122 | (3) |
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123 | (1) |
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123 | (2) |
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PART III: Ligand-Based Virtual Screening Approaches |
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125 | (102) |
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Data Mining Approaches for Enhancement of Knowledge-Based Content of De Novo Chemical Libraries |
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127 | (1) |
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Classification OSAR in Virtual Screening |
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128 | (2) |
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Data Analysis and Visualization |
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130 | (1) |
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131 | (1) |
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131 | (2) |
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133 | (2) |
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Enhancement of Target-Specific Informational Content of Virtual Compound Selections |
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135 | (5) |
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140 | (3) |
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Modeling CYP450-Mediated Drug Metabolism |
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143 | (3) |
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Prediction of Toxicity for Human Fibroblasts |
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146 | (2) |
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148 | (2) |
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De Novo Design of Chemical Libraries |
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150 | (1) |
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151 | (6) |
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151 | (1) |
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152 | (5) |
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Pharmacophore-Based Virtual Screening: A Practical Perspective |
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157 | (1) |
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Motivations Driving the Early Evolution: Historical Developments |
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157 | (3) |
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Concepts and Definition of Terms |
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160 | (8) |
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160 | (1) |
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Virtual Screening, in silico Screening, 3D Database Searching |
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160 | (1) |
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161 | (1) |
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161 | (1) |
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Structure--Activity Relationship |
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162 | (1) |
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162 | (1) |
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Feature, Mapping, Multiple Mappings |
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162 | (1) |
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162 | (1) |
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163 | (1) |
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163 | (1) |
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163 | (1) |
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164 | (1) |
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164 | (1) |
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Geometric Object, Geometric Constraint |
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164 | (1) |
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Projected Point, Receptor Point, Dummy Atoms, Outriggers |
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165 | (1) |
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Steric Constraint, Forbidden Region, Shape-Enhanced Pharmacophores |
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165 | (1) |
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Rigid Match, Rigid Search vs. Flexible Match, Flexible Searching |
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166 | (1) |
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167 | (1) |
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167 | (1) |
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Similarity, Similarity Searching |
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168 | (1) |
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168 | (1) |
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Smiles, Mol Files, SD Files |
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168 | (1) |
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Step 1 --- Constructing 3D Databases |
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168 | (5) |
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Where Do these Lists of Molecules Come from? |
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168 | (1) |
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What Types of Data-Cleaning Must One Perform on the Given Structures? |
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169 | (1) |
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How Does One Handle Chirality, in Particular Chiral Centers with Unspecified Chirality? |
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170 | (1) |
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How Does One Generate the 3D Information? How Does One Handle Conformational Flexibility? |
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171 | (2) |
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Step 2 --- Pharmacophore Discovery |
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173 | (7) |
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How Should the Dataset Be Selected? |
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175 | (1) |
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How Should the Conformational Analysis Be Performed? |
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176 | (1) |
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How Can One Detect Candidate Pharmacophores? |
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176 | (2) |
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What Combinations of Features and Constraints Should Be Used? |
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178 | (1) |
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How Should One Sift through the Candidate Pharmacophores? |
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178 | (1) |
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Are Steric Constraints Important? How Should One Construct Them? |
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179 | (1) |
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Which Computational Approaches Work Best? How Do They Compare? |
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179 | (1) |
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Step 3 --- What Does One Do with the Output of a Virtual Screen? |
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180 | (2) |
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Refine Query --- See if Data that Tests Hypothesis Exists |
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180 | (1) |
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Computational Postprocessing of Hits |
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181 | (1) |
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181 | (1) |
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182 | (12) |
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182 | (2) |
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Fibrinogen Antagonists (Merck) |
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184 | (1) |
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Protein Kinase C Agonists (NCI) |
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185 | (2) |
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HIV Integrase Inhibitors (NCI) |
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187 | (1) |
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Muscarinic M3 Antagonists (Astra) |
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188 | (3) |
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α4β1 Antagonists (Biogen) |
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191 | (2) |
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Estrogen ERα Receptor (Organon) |
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193 | (1) |
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What Are the Components of the Art of Pharmacophore-Based Virtual Screening that Are Crucial for Success? |
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194 | (2) |
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Open Issues and Future Directions |
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196 | (1) |
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197 | (1) |
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198 | (9) |
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201 | (1) |
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202 | (5) |
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Using Pharmacophore Multiplet Fingerprints for Virtual High Throughput Screening |
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207 | (2) |
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209 | (1) |
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210 | (3) |
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210 | (1) |
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210 | (1) |
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211 | (1) |
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212 | (1) |
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212 | (1) |
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213 | (14) |
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213 | (1) |
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Specifying Tuplet Generation Parameters |
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214 | (1) |
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Creating Multiplet Fingerprints |
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215 | (1) |
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Examination of Tuplets for a Class of Active Compounds |
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216 | (1) |
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Clustering Actives By Pharmacophoric Similarity |
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217 | (2) |
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Creating Tuplet Hypotheses and Screening a Database |
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219 | (3) |
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222 | (2) |
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224 | (1) |
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224 | (3) |
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PART IV: Important Considerations Impacting Molecular Docking |
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227 | (74) |
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Potential Functions for Virtual Screening and Ligand Binding Calculations: Some Theoretical Considerations |
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229 | (1) |
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Types of Scoring/Binding Potentials |
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230 | (1) |
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Basic Theory of Absolute and Relative Binding Affinity |
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231 | (5) |
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Absolute Binding Affinity |
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231 | (4) |
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Relative Binding Affinity |
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235 | (1) |
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Screening Potential or Free Energy Calculation? |
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236 | (2) |
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Separability of Binding Free Energy Terms |
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238 | (2) |
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What to Put into Free Energy-Based Scoring Functions |
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240 | (1) |
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Linear Interaction Energy Methods |
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241 | (1) |
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241 | (2) |
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Internal Conformational Changes |
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243 | (3) |
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Effect of Multiple Unbound Ligand Conformations |
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243 | (1) |
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General Effect of Internal Coordinate Changes |
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244 | (2) |
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246 | (3) |
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246 | (1) |
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246 | (3) |
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Solvation-Based Scoring for High Throughput Docking |
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Introduction and Scope of the Problem |
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249 | (1) |
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Evaluating the Free Energy of Binding between Small Molecules and Proteins |
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250 | (2) |
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Scoring Functions for High Throughput Docking |
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252 | (9) |
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Force-Field-Based Schemes |
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253 | (2) |
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Empirically-Based Schemes |
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255 | (3) |
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258 | (1) |
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259 | (2) |
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Full Solvation-Based Scoring for High Throughput Docking |
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261 | (8) |
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The Benefits of Full Solvation-Based Scoring |
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262 | (4) |
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A Full Solvation-Based Scoring Function for High Throughput Docking |
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266 | (3) |
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Implementing Solvation-Based Scoring in a Tiered High Throughput Docking Scheme |
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269 | (3) |
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Conclusions and Future Directions |
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272 | (7) |
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273 | (1) |
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273 | (6) |
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Classification of Ligand-Receptor Complexes Based on Receptor Binding Site Characteristics |
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Marguerita S.L. Lim-Wilby |
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279 | (1) |
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Considerations in Binding Site Definitions |
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280 | (1) |
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Conceptual Advances in Using Binding Site Information |
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281 | (7) |
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Class I --- Small, Well-Defined Binding Sites |
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284 | (1) |
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Class II --- Large, Well-Defined Binding Sites |
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285 | (1) |
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Class III --- Open Binding Site, with Well-Defined Subsites |
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286 | (1) |
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Class IV --- Large Binding Site, with No Well-Defined Subsites |
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286 | (1) |
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Class V --- Shallow and Superficial Binding Site |
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287 | (1) |
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Class VI --- Ill-Defined Binding Site at Hinge Region |
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287 | (1) |
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Protocols for Addressing Binding Sites According to Classes |
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288 | (8) |
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Check that There Are No Obvious Problems with Ligand-Receptor Interactions |
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289 | (1) |
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Check X-ray Pose of Ligand |
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289 | (1) |
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Prepare Ligand for Native Docking |
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289 | (1) |
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Prepare Receptor Structure for Docking |
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289 | (2) |
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291 | (2) |
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Select Level of Site Partitioning |
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293 | (1) |
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Find Best Docking Parameters |
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293 | (1) |
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Number of Conformational Trials per Ligand |
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294 | (1) |
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Option to Perform Minimization with Molecular Mechanics on Saved Poses |
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295 | (1) |
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295 | (1) |
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296 | (5) |
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297 | (1) |
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297 | (4) |
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PART V: Docking Strategies and Algorithms |
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301 | (152) |
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A Practical Guide to Dock 5 |
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303 | (1) |
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304 | (2) |
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304 | (1) |
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Structures from Other Sources |
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304 | (1) |
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Generating Matching Points |
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304 | (1) |
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305 | (1) |
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306 | (1) |
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306 | (1) |
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306 | (1) |
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307 | (4) |
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307 | (1) |
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308 | (1) |
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308 | (1) |
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309 | (1) |
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310 | (1) |
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310 | (1) |
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311 | (6) |
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Generation of Orientations |
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312 | (2) |
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Scoring Ligand Orientations |
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314 | (1) |
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315 | (1) |
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315 | (1) |
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315 | (1) |
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316 | (1) |
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Optimizing Ligand Orientations |
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316 | (1) |
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317 | (4) |
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317 | (2) |
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Rigid Docking of the Anchor |
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319 | (1) |
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319 | (1) |
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320 | (1) |
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321 | (1) |
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321 | (1) |
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321 | (1) |
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321 | (1) |
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Evaluation of Docking Results |
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322 | (1) |
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Reproduction of Crystal Structures |
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322 | (1) |
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322 | (1) |
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Ranking of Docked Molecules |
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323 | (1) |
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323 | (4) |
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324 | (1) |
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324 | (3) |
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Pharmacophore-Based Molecular Docking: A Practical Guide |
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327 | (1) |
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328 | (3) |
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328 | (1) |
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329 | (1) |
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329 | (2) |
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PhDock Database Generation |
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331 | (3) |
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Physical Property Filtering |
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331 | (1) |
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332 | (1) |
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3D Pharmacophore Generation and Overlays |
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332 | (2) |
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334 | (1) |
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334 | (4) |
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334 | (2) |
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336 | (1) |
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Scoring Functions Available |
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336 | (1) |
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336 | (1) |
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Postprocessing Docking Output |
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337 | (1) |
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HIV-1 Protease Test Case (A Cross-Docking Example) |
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338 | (1) |
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339 | (4) |
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DHFR Actives Database for Enrichment-Factor Calculations |
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339 | (1) |
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339 | (1) |
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Dock 4.0 vs. PhDock Enrichment Factors |
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340 | (1) |
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DHFR Actives Seeded into Entire ACD Database |
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340 | (1) |
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Evaluation of Various Scoring Schemes |
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340 | (3) |
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343 | (6) |
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344 | (1) |
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344 | (5) |
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Fragment-Based High Throughput Docking |
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349 | (1) |
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349 | (6) |
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Defining the Binding Site |
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349 | (1) |
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350 | (1) |
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Generation of Ligand Conformations |
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350 | (1) |
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Defining Ligand Positions |
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351 | (1) |
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352 | (1) |
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353 | (1) |
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353 | (1) |
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Binding Energy Function and Postprocessing |
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353 | (1) |
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354 | (1) |
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354 | (1) |
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355 | (9) |
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355 | (2) |
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357 | (3) |
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360 | (3) |
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363 | (1) |
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364 | (15) |
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364 | (2) |
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Preparation of the Library of Compounds |
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366 | (1) |
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367 | (1) |
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368 | (1) |
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368 | (1) |
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369 | (1) |
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370 | (1) |
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370 | (1) |
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Conserved Water Molecules |
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371 | (1) |
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371 | (1) |
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372 | (1) |
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372 | (1) |
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373 | (1) |
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374 | (5) |
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Protein--Ligand Docking and Virtual Screening with Gold |
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379 | (1) |
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379 | (9) |
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380 | (1) |
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380 | (4) |
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384 | (1) |
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Internal Strain and H-Bond Energy |
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384 | (1) |
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Other Aspects of GoldScore |
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384 | (2) |
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386 | (1) |
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386 | (1) |
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Metal Coordination Parameterization |
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387 | (1) |
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Modification of ChemScore for Docking |
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388 | (1) |
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388 | (5) |
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Search Space; Ligand and Protein Flexibility |
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388 | (1) |
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Genetic Algorithm Parameters |
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388 | (1) |
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Chromosome Composition and Ligand Placement |
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389 | (1) |
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390 | (1) |
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Tuning the Genetic Algorithm |
|
|
391 | (1) |
|
Use of Torsion Distributions |
|
|
391 | (2) |
|
|
393 | (10) |
|
Validation Sets for Testing Docking Programs |
|
|
393 | (2) |
|
Interpreting Validation Results |
|
|
395 | (1) |
|
|
396 | (1) |
|
Current Validation Results for Alternative Genetic Algorithm Settings |
|
|
396 | (2) |
|
Importance of Mediating Water Molecules |
|
|
398 | (1) |
|
Performance on Different Classes of Proteins |
|
|
398 | (1) |
|
Prediction of Ligand Affinity; Virtual High Throughput Screening |
|
|
398 | (4) |
|
|
402 | (1) |
|
|
403 | (8) |
|
Protein and Ligand Preparation |
|
|
403 | (1) |
|
|
404 | (1) |
|
|
404 | (1) |
|
|
405 | (1) |
|
Constraints and Restraints |
|
|
405 | (1) |
|
|
405 | (1) |
|
|
405 | (1) |
|
Similarity-Based (Pharmacophore) Restraints |
|
|
406 | (1) |
|
|
407 | (1) |
|
Dealing with Large Numbers of Ligands |
|
|
408 | (1) |
|
|
408 | (1) |
|
Selection of the Best Results |
|
|
409 | (1) |
|
|
410 | (1) |
|
|
411 | (6) |
|
|
411 | (1) |
|
|
411 | (6) |
|
A Brief History of Glide: A New Paradigm for Docking and Scoring in Virtual Screening |
|
|
|
|
|
|
|
417 | (1) |
|
Computational Methodology |
|
|
417 | (1) |
|
Protein and Ligand Preparation |
|
|
418 | (1) |
|
|
419 | (2) |
|
|
421 | (2) |
|
Accuracy in Virtual Screening |
|
|
423 | (3) |
|
|
426 | (9) |
|
|
429 | (1) |
|
|
429 | (2) |
|
p38 Map Kinase (1a9u; 1b17; 1kv2) |
|
|
431 | (4) |
|
Optimizing Glide's Performance |
|
|
435 | (14) |
|
Preparing the Protein Correctly for Glide |
|
|
435 | (1) |
|
Choosing the Protein Site or Sites |
|
|
435 | (1) |
|
Preparing the Site or Sites |
|
|
436 | (1) |
|
Making Sure the Site Properly Accommodates the Cocrystallized Ligand |
|
|
436 | (1) |
|
Choosing the Enclosing Box |
|
|
437 | (1) |
|
Preparing the Ligands Correctly for Glide |
|
|
438 | (1) |
|
Optimizing the vdW Scale Factors |
|
|
439 | (3) |
|
Using Glide to Screen Large Databases |
|
|
442 | (1) |
|
Dividing the Screen Over Multiple Processors |
|
|
442 | (1) |
|
Using glide_sort to Work Up the Results |
|
|
443 | (1) |
|
Dealing with a Subjob that Fails |
|
|
444 | (1) |
|
Using Glide XP to Improve Pose Quality or Enhance Early Enrichment |
|
|
445 | (1) |
|
Using Multiple Receptor Sites to Deal with Receptor Flexibility |
|
|
446 | (3) |
|
|
449 | (4) |
|
|
450 | (1) |
|
|
450 | (3) |
Index |
|
453 | |