| QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method AP Toropova, AA Toropov, JB Veselinović, FN Miljković, AM Veselinović European journal of medicinal chemistry 77, 298-305, 2014 | 73 | 2014 |
| Machine Learning Models for Human In Vivo Pharmacokinetic Parameters with In-House Validation F Miljkovic, A Martinsson, O Obrezanova, B Williamson, M Johnson, ... Molecular pharmaceutics 18 (12), 4520-4530, 2021 | 44 | 2021 |
| Interpretable bilinear attention network with domain adaptation improves drug–target prediction P Bai, F Miljković, B John, H Lu Nature Machine Intelligence 5 (2), 126-136, 2023 | 43 | 2023 |
| Machine learning models for accurate prediction of kinase inhibitors with different binding modes F Miljkovic, R Rodriguez-Perez, J Bajorath Journal of medicinal chemistry 63 (16), 8738-8748, 2019 | 39 | 2019 |
| Structure-and similarity-based survey of allosteric kinase inhibitors, activators, and closely related compounds O Laufkötter, H Hu, F Miljkovic, J Bajorath Journal of Medicinal Chemistry 65 (2), 922-934, 2021 | 35 | 2021 |
| Prediction of in vivo pharmacokinetic parameters and time–exposure curves in rats using machine learning from the chemical structure O Obrezanova, A Martinsson, T Whitehead, S Mahmoud, A Bender, ... Molecular pharmaceutics 19 (5), 1488-1504, 2022 | 28 | 2022 |
| Impact of artificial intelligence on compound discovery, design, and synthesis F Miljković, R Rodríguez-Pérez, J Bajorath ACS omega 6 (49), 33293-33299, 2021 | 28 | 2021 |
| Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature MA Trapotsi, E Mouchet, G Williams, T Monteverde, K Juhani, R Turkki, ... ACS Chemical Biology 17 (7), 1733-1744, 2022 | 26 | 2022 |
| Computational analysis of kinase inhibitors identifies promiscuity cliffs across the human kinome F Miljković, J Bajorath ACS Omega 3 (12), 17295-17308, 2018 | 26 | 2018 |
| Identifying promiscuous compounds with activity against different target classes C Feldmann, F Miljković, D Yonchev, J Bajorath Molecules 24 (22), 4185, 2019 | 23 | 2019 |
| Prediction of different classes of promiscuous and nonpromiscuous compounds using machine learning and nearest neighbor analysis T Blaschke, F Miljkovic, J Bajorath ACS Omega 4 (4), 6883-6890, 2019 | 22 | 2019 |
| Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and … R Rodríguez-Pérez, F Miljković, J Bajorath Journal of Cheminformatics 12, 1-14, 2020 | 17 | 2020 |
| Exploring selectivity of multikinase inhibitors across the human kinome F Miljković, J Bajorath ACS omega 3 (1), 1147-1153, 2018 | 16 | 2018 |
| An overview of recent dipeptidyl peptidase-IV inhibitors: linking their structure and physico-chemical properties with SAR, pharmacokinetics and toxicity A Smelcerovic, F Miljkovic, A Kolarevic, J Lazarevic, A Djordjevic, G Kocic, ... Current Topics in Medicinal Chemistry 15 (23), 2342-2372, 2015 | 15 | 2015 |
| Machine learning in chemoinformatics and medicinal chemistry R Rodríguez-Pérez, F Miljković, J Bajorath Annual review of biomedical data science 5, 43-65, 2022 | 13 | 2022 |
| Data-driven exploration of selectivity and off-target activities of designated chemical probes F Miljković, J Bajorath Molecules 23 (10), 2434, 2018 | 12 | 2018 |
| Reconciling selectivity trends from a comprehensive kinase inhibitor profiling campaign with known activity data F Miljković, J Bajorath ACS omega 3 (3), 3113-3119, 2018 | 12 | 2018 |
| Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction J Gill, M Moullet, A Martinsson, F Miljković, B Williamson, RH Arends, ... CPT: Pharmacometrics & Systems Pharmacology 11 (12), 1560-1568, 2022 | 8 | 2022 |
| Approach for the design of covalent protein kinase inhibitors via focused deep generative modeling A Yoshimori, F Miljković, J Bajorath Molecules 27 (2), 570, 2022 | 8 | 2022 |
| Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome F Miljković, M Vogt, J Bajorath Journal of Computer-Aided Molecular Design 33, 559-572, 2019 | 8 | 2019 |
Jürgen BajorathProfessor of Life Science Informatics, University of BonnVerifierad e-postadress på bit.uni-bonn.de
