Currently on maternity leave.
Senior scientist
Melanie Grandits is senior scientist at the Division of Pharmaceutical Chemistry at the University of Vienna. She is a biotechnologist by training and did her PhD studies in Computational Biology in the field of molecular dynamics simulations. She started her Postdoctoral Research within the K4DD project, where she investigated the kinetics of compounds with the human P-glycoprotein. At the moment she is involved in the eTransafe project, building classification models of transport proteins using machine learning approaches.
Publications
Grandits M, Ecker GF. Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling. Current drug research reviews. 2024;16(2):81-93. Epub 2023. doi: 10.2174/2589977515666230508123041
Smajić A, Grandits M, Ecker GF. Privacy-preserving techniques for decentralized and secure machine learning in drug discovery. Drug Discovery Today. 2023 Dec;28(12):1-8. 103820. Epub 2023 Nov 5. doi: 10.1016/j.drudis.2023.103820
van der Noord VE, van der Stel W, Louwerens G, Verhoeven D, Kuiken HJ, Lieftink C et al. Systematic screening identifies ABCG2 as critical factor underlying synergy of kinase inhibitors with transcriptional CDK inhibitors. Breast Cancer Research. 2023;25(1):51. 51. doi: 10.1186/s13058-023-01648-x
Smajic A, Grandits M, Ecker GF. Using Jupyter Notebooks for re-training machine learning models. Journal of Cheminformatics. 2022 Aug 13;14(1):54. doi: 10.1186/s13321-022-00635-2
Brecklinghaus T, Albrecht W, Kappenberg F, Duda J, Vartak N, Edlund K et al. The hepatocyte export carrier inhibition assay improves the separation of hepatotoxic from non-hepatotoxic compounds. Chemico-Biological Interactions. 2022 Jan 5;351:109728. doi: 10.1016/j.cbi.2021.109728