Dávid Bajusz
Dávid Bajusz
Research Centre for Natural Sciences
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Cited by
Cited by
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
D Bajusz, A Rácz, K Héberger
Journal of Cheminformatics 7, 20, 2015
One-versus two-electron oxidation with peroxomonosulfate ion: reactions with iron (II), vanadium (IV), halide ions, and photoreaction with cerium (III)
G Lente, J Kalmár, Z Baranyai, A Kun, I Kék, D Bajusz, M Takács, L Veres, ...
Inorganic chemistry 48 (4), 1763-1773, 2009
Pharmacologic inhibition of STAT5 in acute myeloid leukemia
B Wingelhofer, B Maurer, EC Heyes, AA Cumaraswamy, A Berger-Becvar, ...
Leukemia 32 (5), 1135-1146, 2018
Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters
A Rácz, D Bajusz, K Héberger
SAR and QSAR in Environmental Research 26 (7-9), 683-700, 2015
Multivariate assessment of lipophilicity scales—computational and reversed phase thin-layer chromatographic indices
F Andrić, D Bajusz, A Rácz, S Šegan, K Héberger
Journal of pharmaceutical and biomedical analysis 127, 81-93, 2016
Structure-Based Virtual Screening Approaches in Kinase-Directed Drug Discovery.
D Bajusz, GG Ferenczy, GM Keserű
Current topics in medicinal chemistry 17 (20), 2235-2259, 2017
Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints
A Rácz, D Bajusz, K Héberger
Journal of cheminformatics 10 (1), 1-12, 2018
Direct targeting options for STAT3 and STAT5 in cancer
A Orlova, C Wagner, ED de Araujo, D Bajusz, HA Neubauer, M Herling, ...
Cancers 11 (12), 1930, 2019
Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification
A Rácz, D Bajusz, K Héberger
Molecules 26 (4), 1111, 2021
Multi-level comparison of machine learning classifiers and their performance metrics
A Rácz, D Bajusz, K Héberger
Molecules 24 (15), 2811, 2019
Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?
A Rácz, A Gere, D Bajusz, K Héberger
RSC advances 8 (1), 10-21, 2018
Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching
D Bajusz, A Rácz, K Héberger
Comprehensive Medicinal Chemistry III 3, 329-378, 2017
Modelling methods and cross-validation variants in QSAR: a multi-level analysis$
A Rácz, D Bajusz, K Héberger
SAR and QSAR in Environmental Research 29 (9), 661-674, 2018
Structural implications of STAT3 and STAT5 SH2 domain mutations
ED de Araujo, A Orlova, HA Neubauer, D Bajusz, HS Seo, ...
Cancers 11 (11), 1757, 2019
Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR
A Rácz, D Bajusz, K Héberger
Molecular informatics 38, 1800154, 2019
Comparison of classification methods with “n-class” receiver operating characteristic curves: A case study of energy drinks
A Rácz, D Bajusz, M Fodor, K Héberger
Chemometrics and Intelligent Laboratory Systems 151, 34-43, 2016
Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
A Rácz, F Andrić, D Bajusz, K Héberger
Metabolomics 14 (3), 1-9, 2018
An electrophilic warhead library for mapping the reactivity and accessibility of tractable cysteines in protein kinases
L Petri, A Egyed, D Bajusz, T Imre, A Hetényi, T Martinek, ...
European Journal of Medicinal Chemistry 207, 112836, 2020
Discovery of Subtype Selective Janus Kinase (JAK) Inhibitors by Structure-Based Virtual Screening
D Bajusz, GG Ferenczy, GM Keseru
Journal of chemical information and modeling 56 (1), 234-247, 2016
DUckCov: a Dynamic Undocking‐based Virtual Screening Protocol for Covalent Binders
M Rachman, A Scarpino, D Bajusz, G Palfy, I Vida, A Perczel, X Barril, ...
ChemMedChem 14, 1011-1021, 2019
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