about
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screeningUSR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniquesistar: a web platform for large-scale protein-ligand dockingUltrafast shape recognition: method and applicationsMachine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical propertiesLow-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.Comments on "leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets": significance for the validation of scoring functions.Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identificationBiochemical evaluation of virtual screening methods reveals a cell-active inhibitor of the cancer-promoting phosphatases of regenerating liver.Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case studyCorrecting the impact of docking pose generation error on binding affinity prediction.Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data.How Reliable Are Ligand-Centric Methods for Target Fishing?Drug repurposing for aging research using model organisms.Performance of machine-learning scoring functions in structure-based virtual screening.A Stochastic Spiking Neural Network for Virtual Screening.Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition.Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours.Unearthing new genomic markers of drug response by improved measurement of discriminative power.Ultrafast shape recognition to search compound databases for similar molecular shapes.The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.Ultrafast shape recognition: Evaluating a new ligand-based virtual screening technologyClassical scoring functions for docking are unable to exploit large volumes of structural and interaction dataMachine Learning for Molecular Modelling in Drug DesignMolTarPred: A web tool for comprehensive target prediction with reliability estimationBuilding Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity
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description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Pedro J Ballester
@es
Pedro J Ballester
@nl
Pedro J Ballester
@sl
Pedro J. Ballester
@en
type
label
Pedro J Ballester
@es
Pedro J Ballester
@nl
Pedro J Ballester
@sl
Pedro J. Ballester
@en
prefLabel
Pedro J Ballester
@es
Pedro J Ballester
@nl
Pedro J Ballester
@sl
Pedro J. Ballester
@en
P1053
A-1148-2008
P106
P1153
8880287000
P1960
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P21
P2798
P31
P3829
P3835
pedro-ballester
P496
0000-0002-4078-743X