| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/JMR.2266 |
| P698 | PubMed publication ID | 23526775 |
| P50 | author | Elizabeth Yuriev | Q42406969 |
| Paul A. Ramsland | Q38317548 | ||
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| Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. | Q45963221 | ||
| Accelerating molecular docking calculations using graphics processing units. | Q45994921 | ||
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| LigDockCSA: protein-ligand docking using conformational space annealing. | Q47579891 | ||
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| Automatic clustering of docking poses in virtual screening process using self-organizing map. | Q48247332 | ||
| Task-parallel message passing interface implementation of Autodock4 for docking of very large databases of compounds using high-performance super-computers. | Q48570844 | ||
| The 5-HT(1A) agonism potential of substituted piperazine-ethyl-amide derivatives is conserved in the hexyl homologues: molecular modeling and pharmacological evaluation. | Q48872842 | ||
| Improving molecular docking through eHiTS' tunable scoring function. | Q51500330 | ||
| PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) protocol: a brief evaluation. | Q51502483 | ||
| Knowledge-based scoring functions in drug design: 3. A two-dimensional knowledge-based hydrogen-bonding potential for the prediction of protein-ligand interactions. | Q51518613 | ||
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| BetaDock: shape-priority docking method based on beta-complex. | Q51555332 | ||
| Consensus virtual screening approaches to predict protein ligands. | Q51561638 | ||
| Fast docking using the CHARMM force field with EADock DSS. | Q51572329 | ||
| MiniMuDS: a new optimizer using knowledge-based potentials improves scoring of docking solutions. | Q51574066 | ||
| Molecular docking with ligand attached water molecules. | Q51583403 | ||
| GPU acceleration of Dock6's Amber scoring computation. | Q51611157 | ||
| Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. | Q51613898 | ||
| Virtual decoy sets for molecular docking benchmarks. | Q51617319 | ||
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| Toward a robust search method for the protein-drug docking problem. | Q51670583 | ||
| Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions. | Q51673917 | ||
| Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0. | Q51680712 | ||
| Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment. | Q51692010 | ||
| Chemical space sampling by different scoring functions and crystal structures. | Q51703673 | ||
| VSDocker: a tool for parallel high-throughput virtual screening using AutoDock on Windows-based computer clusters. | Q51705915 | ||
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| Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. | Q51775733 | ||
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| Docking performance of fragments and druglike compounds. | Q53419359 | ||
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| Protein kinases: docking and homology modeling reliability. | Q54654839 | ||
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| Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise | Q56890026 | ||
| Progress in the structural prediction of G protein-coupled receptors: D3 receptor in complex with eticlopride | Q56979272 | ||
| How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis | Q56984187 | ||
| A Reliable Docking/Scoring Scheme Based on the Semiempirical Quantum Mechanical PM6-DH2 Method Accurately Covering Dispersion and H-Bonding: HIV-1 Protease with 22 Ligands | Q58176559 | ||
| Quantum Mechanics/Molecular Mechanics Strategies for Docking Pose Refinement: Distinguishing between Binders and Decoys in CytochromecPeroxidase | Q58191443 | ||
| Normalizing molecular docking rankings using virtually generated decoys | Q31019586 | ||
| Ligand discovery from a dopamine D3 receptor homology model and crystal structure | Q31031038 | ||
| Identification of inhibitors against p90 ribosomal S6 kinase 2 (RSK2) through structure-based virtual screening with the inhibitor-constrained refined homology model. | Q31034275 | ||
| Docking flexible ligands in proteins with a solvent exposure- and distance-dependent dielectric function | Q33527932 | ||
| Comparison of current docking tools for the simulation of inhibitor binding by the transmembrane domain of the sarco/endoplasmic reticulum calcium ATPase | Q33532330 | ||
| Post-docking virtual screening of diverse binding pockets: comparative study using DOCK, AMMOS, X-Score and FRED scoring functions | Q33540567 | ||
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| Discovery of Novel α4β2 Neuronal Nicotinic Receptor Modulators through Structure-Based Virtual Screening | Q33752770 | ||
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| Structure-based virtual screening of glycogen synthase kinase 3β inhibitors: analysis of scoring functions applied to large true actives and decoy sets. | Q33934472 | ||
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| Molecular modeling: a search for a calpain inhibitor as a new treatment for cataractogenesis | Q34033846 | ||
| A python-based docking program utilizing a receptor bound ligand shape: PythDock. | Q34041244 | ||
| Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening? | Q34049826 | ||
| Affinity of aporphines for the human 5-HT2A receptor: insights from homology modeling and molecular docking studies | Q34052154 | ||
| NNScore 2.0: a neural-network receptor-ligand scoring function | Q34053706 | ||
| AUDocker LE: A GUI for virtual screening with AUTODOCK Vina | Q34057645 | ||
| FReDoWS: a method to automate molecular docking simulations with explicit receptor flexibility and snapshots selection. | Q34173497 | ||
| NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes | Q34240465 | ||
| Antibody recognition of cancer-related gangliosides and their mimics investigated using in silico site mapping | Q34247281 | ||
| Structure based prediction of subtype-selectivity for adenosine receptor antagonists | Q34306247 | ||
| MOLA: a bootable, self-configuring system for virtual screening using AutoDock4/Vina on computer clusters. | Q34331455 | ||
| Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models. | Q34561020 | ||
| Predicting the accuracy of protein-ligand docking on homology models. | Q34669773 | ||
| Docking validation resources: protein family and ligand flexibility experiments | Q34680415 | ||
| An inverse docking approach for identifying new potential anti-cancer targets. | Q34744590 | ||
| Refinement of glucagon-like peptide 1 docking to its intact receptor using mid-region photolabile probes and molecular modeling | Q34963202 | ||
| Virtual screening using molecular simulations | Q34975702 | ||
| Drug efficiency indices for improvement of molecular docking scoring functions. | Q34979608 | ||
| Predictive power of molecular dynamics receptor structures in virtual screening | Q35073109 | ||
| Molecular basis of secretin docking to its intact receptor using multiple photolabile probes distributed throughout the pharmacophore | Q35085038 | ||
| Efficient incorporation of protein flexibility and dynamics into molecular docking simulations | Q35211087 | ||
| Significant enhancement of docking sensitivity using implicit ligand sampling | Q35211103 | ||
| CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. | Q35232599 | ||
| Implementation and evaluation of a docking-rescoring method using molecular footprint comparisons | Q35235591 | ||
| Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy | Q35243615 | ||
| A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series | Q35243650 | ||
| CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. | Q35263970 | ||
| Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions | Q35419556 | ||
| Pharmacological validation of Trypanosoma brucei phosphodiesterases B1 and B2 as druggable targets for African sleeping sickness | Q35587147 | ||
| Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures | Q35630615 | ||
| Statistical potential for modeling and ranking of protein-ligand interactions | Q35635713 | ||
| New aryl hydrocarbon receptor homology model targeted to improve docking reliability | Q35686667 | ||
| A computational approach for exploring carbohydrate recognition by lectins in innate immunity | Q35927649 | ||
| An Improved Weighted-Residue Profile Based Method of Using Protein-Ligand Interaction Information in Increasing Hits Selection from Virtual Screening: A Study on Virtual Screening of Human GPCR A2A Receptor Antagonists | Q36087541 | ||
| Robust scoring functions for protein-ligand interactions with quantum chemical charge models | Q36264705 | ||
| Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction | Q37718843 | ||
| Recent insights on the medicinal chemistry of metal-based compounds: hints for the successful drug design. | Q37789364 | ||
| Quo vadis, virtual screening? A comprehensive survey of prospective applications | Q37798544 | ||
| Accounting for induced-fit effects in docking: what is possible and what is not? | Q37799620 | ||
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| In silico fragment-based drug design | Q38029118 | ||
| P433 | issue | 5 | |
| P304 | page(s) | 215-239 | |
| P577 | publication date | 2013-05-01 | |
| P1433 | published in | Journal of Molecular Recognition | Q3017054 |
| P1476 | title | Latest developments in molecular docking: 2010-2011 in review | |
| P478 | volume | 26 |
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