review article | Q7318358 |
scholarly article | Q13442814 |
P6179 | Dimensions Publication ID | 1020526531 |
P356 | DOI | 10.1007/S12013-016-0769-Y |
P698 | PubMed publication ID | 27796788 |
P50 | author | Mahmoud E. Soliman | Q46812316 |
P2093 | author name string | Neal K Broomhead | |
P2860 | cites work | The Protein Data Bank | Q24515306 |
3DLigandSite: predicting ligand-binding sites using similar structures | Q24621635 | ||
Molecular docking and structure-based drug design strategies | Q26800097 | ||
Accurate calculation of the absolute free energy of binding for drug molecules | Q36427764 | ||
Pocket-based drug design: exploring pocket space | Q36502439 | ||
FINDSITE: a combined evolution/structure-based approach to protein function prediction | Q37215648 | ||
Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures | Q37871418 | ||
Structure-based computational analysis of protein binding sites for function and druggability prediction | Q37971212 | ||
Protein-ligand docking in the new millennium--a retrospective of 10 years in the field | Q38093314 | ||
Beware of docking! | Q38301644 | ||
Free energy via molecular simulation: applications to chemical and biomolecular systems | Q38648060 | ||
Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. | Q45936749 | ||
A molecular dynamics ensemble-based approach for the mapping of druggable binding sites. | Q45981350 | ||
Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules | Q46058456 | ||
Efficient estimation of MMGBSA-based BEs for DNA and aromatic furan amidino derivatives. | Q47285132 | ||
DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment | Q47987315 | ||
MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets | Q48167441 | ||
Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites | Q48498889 | ||
Predicting functionally important residues from sequence conservation. | Q50686129 | ||
Structure-based target druggability assessment. | Q51252555 | ||
Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. | Q51922140 | ||
Structure-based maximal affinity model predicts small-molecule druggability. | Q51925540 | ||
Free Energy Calculations by the Molecular Mechanics Poisson-Boltzmann Surface Area Method | Q88031278 | ||
The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities | Q27012984 | ||
The ChEMBL bioactivity database: an update | Q27144224 | ||
UCSF Chimera--a visualization system for exploratory research and analysis | Q27860666 | ||
Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features | Q27902352 | ||
Automatic generation of bioinformatics tools for predicting protein-ligand binding sites | Q28602184 | ||
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings | Q28842973 | ||
The druggable genome | Q29547361 | ||
Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models | Q29616389 | ||
PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins. | Q30374520 | ||
EasyMIFS and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures. | Q30381172 | ||
Binding site detection and druggability prediction of protein targets for structure-based drug design | Q30422561 | ||
Structural and energetic analysis of 2-aminobenzimidazole inhibitors in complex with the hepatitis C virus IRES RNA using molecular dynamics simulations | Q30582034 | ||
Docking challenge: protein sampling and molecular docking performance | Q30607323 | ||
Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. | Q30835329 | ||
Prediction of functional sites based on the fuzzy oil drop model | Q33285864 | ||
Drug-like Density: A Method of Quantifying the “Bindability” of a Protein Target Based on a Very Large Set of Pockets and Drug-like Ligands from the Protein Data Bank | Q33453085 | ||
Prediction of ligand‐binding sites of proteins by molecular docking calculation for a random ligand library | Q33743244 | ||
KVFinder: steered identification of protein cavities as a PyMOL plugin. | Q33809778 | ||
Comparison of computational methods to model DNA minor groove binders. | Q33837267 | ||
GalaxySite: ligand-binding-site prediction by using molecular docking | Q33860851 | ||
A critical comparative assessment of predictions of protein-binding sites for biologically relevant organic compounds | Q33897575 | ||
Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics | Q33942859 | ||
DrugPred: a structure-based approach to predict protein druggability developed using an extensive nonredundant data set. | Q34047555 | ||
Molecular dynamics simulations and drug discovery | Q34061019 | ||
In silico prediction of binding sites on proteins | Q34099513 | ||
Understanding and predicting druggability. A high-throughput method for detection of drug binding sites | Q34129648 | ||
Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics | Q34300133 | ||
On the nature of cavities on protein surfaces: application to the identification of drug-binding sites | Q34494450 | ||
Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations | Q34531578 | ||
Binding site detection and druggability index from first principles. | Q34964806 | ||
Identifying and characterizing binding sites and assessing druggability | Q34980548 | ||
Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. | Q35206344 | ||
Solvent interaction energy calculations on molecular dynamics trajectories: increasing the efficiency using systematic frame selection | Q35540473 | ||
Comparison of different ranking methods in protein-ligand binding site prediction. | Q36197297 | ||
P433 | issue | 1 | |
P921 | main subject | computational biology | Q177005 |
ligand binding | Q61659151 | ||
P304 | page(s) | 15-23 | |
P577 | publication date | 2016-10-31 | |
P1433 | published in | Cell Biochemistry and Biophysics | Q15755135 |
P1476 | title | Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites | |
P478 | volume | 75 |
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Q52590532 | Alcohol Metabolic Inefficiency: Structural Characterization of Polymorphism-Induced ALDH2 Dysfunctionality and Allosteric Site Identification for Design of Potential Wildtype Reactivators. |
Q98294742 | Druggability assessment for selected serine proteases in a pharmaceutical industry setting |
Q55709846 | Identification of new allosteric sites and modulators of AChE through computational and experimental tools. |
Q98568595 | Ligand binding free-energy calculations with funnel metadynamics |
Q57009477 | P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure |
Q47232343 | Possible binding sites and interactions of propanidid and AZD3043 within the γ-aminobutyric acid type A receptor (GABAAR). |
Q101038845 | Spatiotemporal identification of druggable binding sites using deep learning |
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