A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking
about
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screeningMolecular docking and structure-based drug design strategiesSystems pharmacology: network analysis to identify multiscale mechanisms of drug actionStructure-based virtual screening for drug discovery: a problem-centric reviewOpen Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery fieldSupport vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical librariesCharacterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screeningA machine learning-based method to improve docking scoring functions and its application to drug repurposingistar: a web platform for large-scale protein-ligand dockingUltrafast shape recognition: method and applicationsApplication of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1)Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacologyOpen source molecular modelingCheminformatics Research at the Unilever Centre for Molecular Science Informatics CambridgePredictions of Ligand Selectivity from Absolute Binding Free Energy CalculationsCSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.Neural Networks for the Prediction of Organic Chemistry Reactions.Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.Using RosettaLigand for small molecule docking into comparative models.Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferationRandom forests for genomic data analysis.Machine learning methods in chemoinformaticsHierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identificationOptimization of molecular docking scores with support vector rank regression.LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information aloneMachine-learning techniques applied to antibacterial drug discoveryBgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes.Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case studyMachine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein StructuresBioinformatics and variability in drug response: a protein structural perspective.Comprehensive prediction of drug-protein interactions and side effects for the human proteome.Correcting the impact of docking pose generation error on binding affinity prediction.Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors.Potential Broad Spectrum Inhibitors of the Coronavirus 3CLpro: A Virtual Screening and Structure-Based Drug Design Study.Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.Improved estimation of protein-ligand binding free energy by using the ligand-entropy and mobility of water moleculesInformatics, machine learning and computational medicinal chemistry.
P2860
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P2860
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking
description
2010 nî lūn-bûn
@nan
2010 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
A machine learning approach to ...... lications to molecular docking
@ast
A machine learning approach to ...... lications to molecular docking
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A machine learning approach to ...... lications to molecular docking
@nl
type
label
A machine learning approach to ...... lications to molecular docking
@ast
A machine learning approach to ...... lications to molecular docking
@en
A machine learning approach to ...... lications to molecular docking
@nl
prefLabel
A machine learning approach to ...... lications to molecular docking
@ast
A machine learning approach to ...... lications to molecular docking
@en
A machine learning approach to ...... lications to molecular docking
@nl
P2860
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A machine learning approach to ...... lications to molecular docking
@en
P2093
John B O Mitchell
Pedro J Ballester
P2860
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P3181
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10.1093/BIOINFORMATICS/BTQ112
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P577
2010-05-01T00:00:00Z