about
Recent Progress and Future Directions in Protein-Protein DockingClassification and Exploration of 3D Protein Domain Interactions Using Kbdock.Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experimentRepresenting and comparing protein folds and fold families using three-dimensional shape-density representations.DockTrina: docking triangular protein trimers.Identification and characterisation of a novel immune-type receptor (NITR) gene cluster in the European sea bass, Dicentrarchus labrax, reveals recurrent gene expansion and diversification by positive selection.Comparison of ligand-based and receptor-based virtual screening of HIV entry inhibitors for the CXCR4 and CCR5 receptors using 3D ligand shape matching and ligand-receptor docking.Discovery of novel HIV entry inhibitors for the CXCR4 receptor by prospective virtual screening.Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods.gEMpicker: a highly parallel GPU-accelerated particle picking tool for cryo-electron microscopy.Blind prediction of interfacial water positions in CAPRIProtein-protein docking by fast generalized Fourier transforms on 5D rotational manifolds.Identifying and characterizing promiscuous targets: implications for virtual screening.Recent trends and future prospects in computational GPCR drug discovery: from virtual screening to polypharmacology.KBDOCK 2013: a spatial classification of 3D protein domain family interactions.Using Spherical Harmonic Surface Property Representations for Ligand-Based Virtual Screening.GESSE: Predicting Drug Side Effects from Drug-Target Relationships.Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex.A structure-based classification and analysis of protein domain family binding sites and their interactions.gEMfitter: a highly parallel FFT-based 3D density fitting tool with GPU texture memory acceleration.HexServer: an FFT-based protein docking server powered by graphics processors.Accelerating and focusing protein-protein docking correlations using multi-dimensional rotational FFT generating functions.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.GES polypharmacology fingerprints: a novel approach for drug repositioning.Spatial clustering of protein binding sites for template based protein docking.Predicting drug polypharmacology using a novel surface property similarity-based approach.Detecting drug promiscuity using Gaussian ensemble screening.Ultra-fast FFT protein docking on graphics processors.Protein docking using case-based reasoning.Flexible protein docking refinement using pose-dependent normal mode analysis.Exploring c-Met kinase flexibility by sampling and clustering its conformational space.Toward high throughput 3D virtual screening using spherical harmonic surface representations.Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2.Modeling the structural basis of human CCR5 chemokine receptor function: from homology model building and molecular dynamics validation to agonist and antagonist dockingUsing consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening
P50
Q22241335-7E57B293-4B85-4AE0-BB38-E37483463547Q30387343-1732DFE9-3E03-4412-B3AB-EDF28351C3A4Q30387414-DA4E9249-E038-41B0-BB31-7D33B14E3F99Q30409446-3C6BD987-2F77-4E92-860F-2CC700530779Q30432061-6D5ED1F8-8A0B-4BEF-B8DB-1607E07D7083Q30496032-E9272514-BA9C-47A7-BA54-32A23E749D36Q33321007-9BCA0BD2-C642-4D08-A213-9B5CD8F644ABQ33428717-C997CDEC-C01F-492B-9203-E6742AC0AA14Q33751455-E98B1B6B-C74E-4862-A1B9-9E20700F4811Q35021962-A18EBEAA-4B2A-469B-9265-4B10CD7670D4Q36090741-9AB584A1-88EB-4D67-B956-B883791D9DEDQ37142683-AA5842E0-2151-437B-AABF-BB082AB45DEEQ37999084-97804607-A829-42CB-8E5A-460E07D6FF79Q38104820-3E75DB06-D2D3-403F-BC13-9C0FD0F6B5AAQ38440719-858E377D-45A2-4F53-A634-D6B70995A5E7Q39551490-1F1B22D8-C8A5-4AF6-A80F-A3D8920E853CQ40648966-EC06FFF4-DB70-4980-8791-0A4C8555FA88Q40883348-F9305715-368A-4085-AFC4-CB912FFD5C51Q41148567-C14E7BA6-5AF7-4E98-BDF2-B212ECF8074CQ41610179-105A8479-4B25-447E-9D62-84C9EA2562DDQ42011577-2AF8B8CB-9BFC-4E24-825E-7DC13C24E81BQ43063724-A8D7DCE4-298D-4BBC-92F0-34D6AC40D439Q43638639-84A4F393-43AE-46A5-BBDD-95A47BFDC361Q43883641-9243D689-9ACD-4C14-B182-719AA23A7F5BQ44453669-D6C9C489-2373-4487-9F9E-B18CD81B3FADQ47192989-BC52695E-C929-4AB4-88B5-2BB9901136F1Q47434343-16B37350-78AC-48D9-8FF2-7F0705C6D4CBQ48191415-5247E8E1-3460-44C5-9FD3-7EE5E5ACE3CCQ51151900-BE50B016-552E-4ADF-9198-E03B95439C4FQ51366191-8A8293F5-D60F-47AC-A57F-5877710B9023Q51438298-3ECF4126-B3FC-4FBC-92F4-7DC45735296DQ51906445-67774087-5336-4F2A-A2C9-0B3DA3CA5DB8Q52015248-D6FCBC44-6661-43FD-B91D-2222FA924298Q83372802-FCA134B1-F353-4184-88A8-F81321C24A3AQ84173142-6162F1B3-A518-4867-9415-F1C2EEC2E7F8
P50
description
hulumtues
@sq
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
David W. Ritchie
@ast
David W. Ritchie
@en
David W. Ritchie
@es
David W. Ritchie
@nl
David W. Ritchie
@sl
type
label
David W. Ritchie
@ast
David W. Ritchie
@en
David W. Ritchie
@es
David W. Ritchie
@nl
David W. Ritchie
@sl
prefLabel
David W. Ritchie
@ast
David W. Ritchie
@en
David W. Ritchie
@es
David W. Ritchie
@nl
David W. Ritchie
@sl
P106
P1153
7201711065
P21
P2456
P31
P496
0000-0002-0906-7354