about
Multiple conformational states in retrospective virtual screening – homology models vs. crystal structures: beta-2 adrenergic receptor case studyRobust optimization of SVM hyperparameters in the classification of bioactive compoundsThe influence of negative training set size on machine learning-based virtual screening.The influence of the inactives subset generation on the performance of machine learning methodsRational design in search for 5-phenylhydantoin selective 5-HT7R antagonists. Molecular modeling, synthesis and biological evaluation.Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.Exploiting uncertainty measures in compounds activity prediction using support vector machines.Quo vadis G protein-coupled receptor ligands? A tool for analysis of the emergence of new groups of compounds over time.Imidazolidine-4-one derivatives in the search for novel chemosensitizers of Staphylococcus aureus MRSA: synthesis, biological evaluation and molecular modeling studies.Structural modifications of the serotonin 5-HT7 receptor agonist N-(4-cyanophenylmethyl)-4-(2-biphenyl)-1-piperazinehexanamide (LP-211) to improve in vitro microsomal stability: A case study.Extremely Randomized Machine Learning Methods for Compound Activity Prediction.Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-HT(7).An application of machine learning methods to structural interaction fingerprints--a case study of kinase inhibitors.Application of Structural Interaction Fingerpints (SIFts) into post-docking analysis - insight into activity and selectivity.The influence of hashed fingerprints density on the machine learning methods performance.Fingerprint-based consensus virtual screening towards structurally new 5-HT(6)R ligands.MetStabOn-Online Platform for Metabolic Stability Predictions.Structural insights into serotonin receptor ligands polypharmacology.A multidimensional analysis of machine learning methods performance in the classification of bioactive compoundsSubstructural Connectivity Fingerprint and Extreme Entropy Machines—A New Method of Compound Representation and AnalysisThe influence of training actives/inactives ratio on machine learning performance5-Arylideneimidazolones with Amine at Position 3 as Potential Antibiotic Adjuvants against Multidrug Resistant BacteriaThree-dimensional descriptors for aminergic GPCRs: dependence on docking conformation and crystal structureHow Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning TechniquesDevelopment of New Methods Needs Proper Evaluation-Benchmarking Sets for Machine Learning Experiments for Class A GPCRs
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description
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Sabina Smusz
@ast
Sabina Smusz
@en
Sabina Smusz
@es
Sabina Smusz
@nl
Sabina Smusz
@sl
type
label
Sabina Smusz
@ast
Sabina Smusz
@en
Sabina Smusz
@es
Sabina Smusz
@nl
Sabina Smusz
@sl
prefLabel
Sabina Smusz
@ast
Sabina Smusz
@en
Sabina Smusz
@es
Sabina Smusz
@nl
Sabina Smusz
@sl
P106
P214
321144782746309037908
P2456
P31
P496
0000-0002-2891-5603
P7859
viaf-321144782746309037908