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Dávid Bajusz
Dávid Bajusz
HUN-REN Research Centre for Natural Sciences
Verified email at ttk.hu - Homepage
Title
Cited by
Cited by
Year
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
D Bajusz, A Rácz, K Héberger
Journal of Cheminformatics 7, 20, 2015
14582015
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
2872021
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
2342009
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
1592018
Multi-level comparison of machine learning classifiers and their performance metrics
A Rácz, D Bajusz, K Héberger
Molecules 24 (15), 2811, 2019
1242019
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
1192015
Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints
A Rácz, D Bajusz, K Héberger
Journal of cheminformatics 10, 1-12, 2018
1092018
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
872017
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
842019
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
692016
Comprehensive medicinal chemistry III
D Bajusz, A Rácz, K Héberger
Elsevier, Amsterdam, The Netherlands, 2017
652017
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
64*2017
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
612019
Exploring protein hotspots by optimized fragment pharmacophores
D Bajusz, WS Wade, G Satała, AJ Bojarski, J Ilaš, J Ebner, F Grebien, ...
Nature Communications 12 (1), 3201, 2021
532021
Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristics
RA Miranda-Quintana, D Bajusz, A Rácz, K Héberger
Journal of cheminformatics 13 (1), 32, 2021
532021
Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR
A Rácz, D Bajusz, K Héberger
Molecular informatics 38, 1800154, 2019
512019
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
492018
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
452018
Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection
RA Miranda-Quintana, A Rácz, D Bajusz, K Héberger
Journal of Cheminformatics 13 (1), 33, 2021
422021
Machine learning models for classification tasks related to drug safety
A Rácz, D Bajusz, RA Miranda-Quintana, K Héberger
Molecular Diversity 25 (3), 1409-1424, 2021
392021
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