| Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? D Bajusz, A Rácz, K Héberger Journal of Cheminformatics 7, 20, 2015 | 1229 | 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 | 217 | 2009 |
| 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 | 211 | 2021 |
| 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 | 142 | 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 | 107 | 2015 |
| Multi-level comparison of machine learning classifiers and their performance metrics A Rácz, D Bajusz, K Héberger Molecules 24 (15), 2811, 2019 | 95 | 2019 |
| Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints A Rácz, D Bajusz, K Héberger Journal of cheminformatics 10, 1-12, 2018 | 94 | 2018 |
| 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 | 81 | 2017 |
| 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 | 75 | 2019 |
| 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 | 65 | 2016 |
| 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 | 56 | 2019 |
| Comprehensive medicinal chemistry III D Bajusz, A Rácz, K Héberger Elsevier, Amsterdam, The Netherlands, 2017 | 54 | 2017 |
| 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 | 54* | 2017 |
| Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR A Rácz, D Bajusz, K Héberger Molecular informatics 38, 1800154, 2019 | 48 | 2019 |
| 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 | 45 | 2021 |
| 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 | 41 | 2021 |
| 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 | 40 | 2018 |
| 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 | 39 | 2018 |
| 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 | 36 | 2021 |
| 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 | 34 | 2020 |
Karoly HebergerELKH Research Centre for Natural Sciences, Institute of Excellence, Hungarian Academy of Sciences在 chemres.hu 的电子邮件经过验证
