This page contains an up to date list of the scientific research with my contribution. Each research piece is supported by a source link to a web page where further material can be downloaded. Where possible, blog posts or newsletters mentioning the specific research are provided as well. In case of an outdated link - a highly unlikely possibility - feel free to contact me or perform a search under authors' names.

Publications

  • Miljković, F.*; Bajorath, J. Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence. Mol. Pharmaceutics 2024, in press. [PDF]
  • Gawehn, E.; Greene, E.; Miljković, F.; Obrezanova, O.; Subramanian, V.; Trapotsi, M.-A.; Winiwarter, S. Perspectives on the Use of Machine Learning for ADME Prediction at AstraZeneca. Xenobiotica 2024, 54, 368-378. [PDF]
  • Miljković, F.*; Medina-Franco, J. L. Artificial Intelligence-Open Science Symbiosis in Chemoinformatics. Artif. Intell. Life Sci. 2024, 5, 100096. [PDF]
  • Chen, Y.; Seidel, T.; Jacob, R. A.; Hirte, S.; Mazzolari, A.; Pedretti, A.; Vistoli, G.; Langer, T; Miljković, F.; Kirchmair, J. Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation. J. Chem. Inf. Model. 2024, 64, 348-358. [PDF]
  • Bajorath, J.; Gardner, S.; Grisoni, F.; Horta Andrade, C.; Kirchmair, J.; Landon, M.; Medina-Franco, J.-L.; Miljković, F.; Montanari, F.; Rodríguez-Pérez, R. First-generation Themed Article Collections. Artif. Intell. Life Sci. 2023, 4, 100088. [PDF]
  • Xerxa, E.; Miljković, F.; Bajorath, J. Data-Driven Global Assessment of Protein Kinase Inhibitors with Emphasis on Covalent Compounds. J. Med. Chem. 2023, 66, 7657–7665. [PDF]
  • Bai, P.; Miljković, F.; John, B.; Lu, H. Interpretable Bilinear Attention Network with Domain Adaptation Improves Drug–Target Prediction. Nat. Mach. Intell. 2023, 5, 126-136. [PDF]
  • Gill, G.; Moullet, M.; Martinsson, A.; Miljković, F., Williamson, B.; Arends, R. H.; Pilla Reddy, V. Evaluating the Performance of Machine-Learning Regression Models for Pharmacokinetic Drug–Drug Interactions. CPT: Pharmacomet. Syst. Pharmacol. 2023, 1, 122-134. [PDF]
  • Gill, G.; Moullet, M.; Martinsson, A.; Miljković, F., Williamson, B.; Arends, R. H.; Pilla Reddy, V. Comparing the Applications of Machine Learning, PBPK, and Population Pharmacokinetic Models in Pharmacokinetic Drug–Drug Interaction Prediction. CPT: Pharmacomet. Syst. Pharmacol. 2022, 11, 1560-1568. [PDF]
  • Martínez Mora, A.; Mogemark, M.; Subramanian, V.; Miljković, F.* Interpretation of Multi-Task Clearance Models from Molecular Images Supported by Experimental Design. Artif. Intell. Life Sci. 2022, 2, 100048. [PDF]
  • Trapotsi, M.-A.; Mouchet, E.; Williams, G.; Monteverde, T.; Juhani, K.; Turkki, R.; Miljković, F.; Martinsson, A.; Mervin, L; Pryde, K. R.; Müllers, E.; Barrett, I.; Engkvist, O.; Bender, A; Moreau, K. Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature. ACS Chem. Biol. 2022, 17, 1733-1744. [PDF]
  • Martínez Mora, A.; Subramanian, V.; Miljković, F.* Multi-Task Convolutional Neural Networks for Predicting In Vitro Clearance Endpoints from Molecular Images. J. Comput. Aided Mol. Des. 2022, 36, 443-457. [PDF]
  • Obrezanova, O.; Martinsson, A.; Whitehead, T.; Mahmoud, S.; Bender, A.; Miljković, F.; Grabowski, P.; Irwin, B.; Oprisiu, I.; Conduit, G.; Segall, M.; Smith, G. F.; Williamson, B.; Winiwarter, S.; Greene N. Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure. Mol. Pharmaceutics 2022, 19, 1488-1504. [PDF]
  • Rodríguez-Pérez, R.; Miljković, F.; Bajorath, J. Machine Learning in Chemoinformatics and Medicinal Chemistry. Ann. Rev. Biomed. Data Sci. 2022, 5, 43-65. [PDF]
  • Laufkötter, O.; Hu, H.; Miljković, F.; Bajorath, J. Structure- and Similarity-Based Survey of Allosteric Kinase Inhibitors, Activators, and Closely Related Compounds. J. Med. Chem. 2022, 65, 922-934. [PDF]
  • Yoshimori, A.; Miljković, F.; Bajorath, J. Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling. Molecules 2022, 27, e570. [PDF]
  • Miljković, F.; Rodríguez-Pérez, R.; Bajorath, J. Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis. ACS Omega 2021, 6, 33293-33299. [PDF]
  • Miljković, F.*; Martinsson, A.; Obrezanova, O.; Williamson, B.; Johnson, M.; Sykes, A.; Bender, A.; Greene, N. Machine Learning Models for Human In Vivo Pharmacokinetic Parameters with In-House Validation. Mol. Pharmaceutics 2021, 18, 4520-4530. [PDF]
  • Hu, H.; Laufkötter, O.; Miljković, F.; Bajorath, J. Data Set of Competitive and Allosteric Protein Kinase Inhibitors Confirmed by X-ray Crystallography. Data in Brief 2021, 35, e106816. [PDF]
  • Hu, H.; Laufkötter, O.; Miljković, F.; Bajorath, J. Systematic Comparison of Competitive and Allosteric Kinase Inhibitors Reveals Common Structural Characteristics. Eur. J. Med. Chem. 2021, 214, e113206. [PDF]
  • Miljković, F.; Chaudhari, R. Members of our Early Career Panel Highlight Key Research Articles on the Theme of Computer-Aided Drug Design. Future Drug Discov. 2020, 2, FDD52. [PDF]
  • Rodríguez-Pérez, R.; Miljković, F.; Bajorath, J. Assessing the Information Content of Structural and Protein–Ligand Interaction Representations for the Classification of Kinase Inhibitor Binding Modes via Machine Learning and Active Learning. J. Cheminform. 2020, 12, e36. [PDF]
  • Miljković, F.; Xiong, R.; Sivakumar, D.; Brown, C. A. Members of our Early Career Panel Highlight Key Research Articles on the Theme of Drug Repurposing. Future Drug Discov. 2020, 2, FDD39. [PDF]
  • Miljković, F.; Rodríguez-Pérez, R.; Bajorath, J. Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes. J. Med. Chem. 2020, 63, 8738-8748. [PDF] [Drug Hunter Post] [VLS3D.com: Directory of Tools & Databases] [DrugAI Post]
  • Miljković, F.; Bajorath, J. Data Structures for Computational Compound Promiscuity Analysis and Exemplary Applications to Inhibitors of the Human Kinome. J. Comput. Aided Mol. Des. 2020, 34, 1-10. [PDF]
  • González-Medina, M.; Miljković, F.; Haase, G. S.; Drueckes, P.; Trappe, J; Laufer, S; Bajorath, J. Promiscuity Analysis of a Kinase Panel Screen with Designated p38 alpha Inhibitors. Eur. J. Med. Chem. 2020, 187, 112004. [PDF]
  • Feldmann, C.; Miljković, F.; Yonchev, D.; Bajorath, J. Identifying Promiscuous Compounds with Activity Against Different Target Classes. Molecules 2019, 24, e4185. [PDF]
  • Miljković, F.; Bajorath, J. Data Structures for Compound Promiscuity Analysis: Cliffs, Pathways, and Hubs Formed by Inhibitors of the Human Kinome. Future Sci. OA 2019, 5, FSO404. [PDF]
  • Miljković, F.; Vogt, M.; Bajorath, J. Systematic Computational Identification of Promiscuity Cliff Pathways Formed by Inhibitors of the Human Kinome. J. Comput. Aided Mol. Des. 2019, 33, 559-572. [PDF]
  • Blaschke, T.; Miljković, F.; Bajorath, J. Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis. ACS Omega 2019, 4, 6883-6890. [PDF]
  • Miljković, F.; Bajorath, J. Computational Analysis of Kinase Inhibitors Identifies Promiscuity Cliffs across the Human Kinome. ACS Omega 2018, 3, 17295–17308. [PDF]
  • Miljković, F.; Bajorath, J. Data-driven Exploration of Selectivity and Off-target Activities of Designated Chemical Probes. Molecules 2018, 23, e2434. [PDF]
  • Miljković, F.; Bajorath, J. Evaluation of Kinase Inhibitor Selectivity Using Cell-Based Profiling Data. Mol. Inform. 2018, 37, e1800024. [PDF]
  • Miljković, F.; Bajorath, J. Reconciling Selectivity Trends from a Comprehensive Kinase Inhibitor Profiling Campaign with Known Activity Data. ACS Omega 2018, 3, 3113-3119. [PDF]
  • Miljković, F.; Bajorath, J. Exploring Selectivity of Multikinase Inhibitors across the Human Kinome. ACS Omega 2018, 3, 1147-1153. [PDF]
  • Miljković, F.; Kunimoto, R.; Bajorath, J. Identifying Relationships between Unrelated Pharmaceutical Target Proteins on the Basis of Shared Active Compounds Future Sci. OA 2017, 3, FSO212. [PDF] [RxNet Post]
  • Smelcerovic, A.; Miljkovic, F.; Kolarevic, A.; Lazarevic, J.; Djordjevic, A.; Kocic, G.; Anderluh, M. An Overview of Recent Dipeptidyl Peptidase-IV Inhibitors: Linking Their Structure and Physico-Chemical Properties with SAR, Pharmacokinetics and Toxicity. Curr. Top. Med. Chem. 2015, 15, 2342-2372. [PDF]
  • Toropov, A. A.; Veselinović, J. B.; Veselinović, A. M.; Miljković, F. N.; Toropova, A. P. QSAR Models for 1,2,4-Benzotriazines as Src Inhibitors Based on Monte Carlo Method. Med. Chem. Res. 2015, 24, 283-290. [PDF]
  • Toropova, A. P.; Toropov, A. A.; Veselinović, J. B.; Miljković, F. N.; Veselinović, A. M. QSAR Models for HEPT Derivates as NNRTI Inhibitors Based on Monte Carlo Method. Eur. J. Med. Chem. 2014, 77, 298-305. [PDF]

Conferences

  • “Chemoinformatics Strasbourg Summer School”, 25 June – 29 June 2018, University of Strasbourg, Strasbourg, France. Poster: Miljković, F.; Bajorath, J. “Exploring Selectivity of Multi-kinase Inhibitors across the Human Kinome”, awarded as the best poster by public choice. [Poster] [Abstract] [Newsletter]
  • “11th International Conference “Physical Chemistry 2012”, 24 September – 28 September 2012, Society of Physical Chemists of Serbia, Belgrade, Serbia. Conference Paper: Nikolić, G.M.; Veselinović, A. M.; Mitić, Ž. J.; Miljković, F. S. Application of Multivariate Curve Resolution-alternating Least Squares (MCRALS) Method for the Study of Cu(II) Ion Influence on the Pyrogallol Autoxidation in Aqueous Solution. Proceedings of the 11th International Conference on Fundamental and Applied Aspects of Physical Chemistry 2012, 1, 188-189 [Conference Paper]
  • “4th BBBB International Conference on Pharmaceutical Sciences”, 29 September – 1 October 2011, Slovenian Pharmaceutical Society, Bled, Slovenia. Conference Paper: Nikolić, G. M.; Živković, J. V.; Nikolić, M. G; Miljković, F. Synergism in the Extraction of Paracetamol from the Aqueous NaCl Solutions by the Diethyl Ether/1-Butanol Binary Solvent Mixtures. Eur. J. Med. Chem. 2011, 44(S1), 183-184 [Conference Paper]

Software

  • Miljković, F.; Rodríguez-Pérez, R.; Bajorath, J. Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. [Download]

Data Sets

  • Feldmann, C.; Miljković, F.; Yonchev, D.; Bajorath, J. Promiscuous compounds with activity against different target classes. [Download]
  • Miljković, F.; Bajorath, J. Promiscuity cliffs (PCs), promiscuity cliff pathways (PCPs), and promiscuity hubs (PHs) formed by inhibitors of human kinases. [Download]
  • Miljković, F.; Bajorath, J. Selectivity profiling of multi-kinase inhibitors across the Human Kinome from ChEMBL. [Download]
  • Miljković, F.; Bajorath, J. 286 new target pairs based on shared compounds from ChEMBL. [Download]

Doctoral Thesis

  • Miljković, F. Chemoinformatics-Driven Approaches for Kinase Drug Discovery. Universitäts- und Landesbibliothek Bonn 2020. [PDF]