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Attributs	Valeurs
type	work Issue
Is Part Of	http://hub.abes.fr/acs/periodical/jcisd8/2008/volume_48
has manifestation of work	http://hub.abes.fr/acs/periodical/jcisd8/2008/volume_48/issue_11/m/print http://hub.abes.fr/acs/periodical/jcisd8/2008/volume_48/issue_11/m/web
Date Copyrighted	2008
Rights	Copyright © 2008 American Chemical Society
issue	11
Rights Holder	American Chemical Society
is Is Part Of of	Targets Looking for Drugs: A Multistep Computational Protocol for the Development of Structure-Based Pharmacophores and Their Applications for Hit Discovery Chemical Markup, XML and the World-Wide Web. 8. Polymer Markup Language Development of an Efficient Geometry Optimization Method for Water Clusters Legitimate Utilization of Large Descriptor Pools for QSPR/QSAR Models Toward an Improved Clustering of Large Data Sets Using Maximum Common Substructures and Topological Fingerprints Comparative Study of Topological Indices of Macro/Supramolecular RNA Complex Networks In Silico Mutagenesis and Docking Studies of Pseudomonas aeruginosa PA-IIL Lectin  Predicting Binding Modes and Energies 3D QSAR/CoMFA and CoMSIA Studies on Antileukemic Steroidal Esters Coupled with Conformationally Flexible Nitrogen Mustards FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set Clustering and Classifying Diverse HIV Entry Inhibitors Using a Novel Consensus Shape-Based Virtual Screening Approach: Further Evidence for Multiple Binding Sites within the CCR5 Extracellular Pocket Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space Structure-Based Approach for the Study of Estrogen Receptor Binding Affinity and Subtype Selectivity External Validation and Prediction Employing the Predictive Squared Correlation Coefficient  Test Set Activity Mean vs Training Set Activity Mean Virtual Screening for R-Groups, including Predicted pIC50 Contributions, within Large Structural Databases, Using Topomer CoMFA Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening Protein−Ligand Docking against Non-Native Protein Conformers Identification and Selection of “Privileged Fragments” Suitable for Primary Screening

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