Post Doc
Aljoša Smajić obtained a Ph.D. in the field of Pharmacy from the University of Vienna and has commenced a postdoctoral research position within the Pharmacoinformatics Research Group. His research will focus on applying data science methodologies to develop in silico predictive and translational safety models, leveraging advanced machine learning and deep learning algorithms.
Publications
Showing entries 1 - 6 out of 6
Cronin MTD, Basiri H, Belfield SJ, Chavan S, Chrysochoou G, Enoch SJ et al. The Findable, Accessible, Interoperable, Reusable (FAIR) Lite Principles to ensure utility of computational toxicology models. Altex. 2025 Sept 25. Epub 2025 Sept 25. doi: 10.14573/altex.2502021
Miele M, Smajić A, Pace V. The Versatility of the Roskamp Homologation in Synthesis. Molecules. 2025 Mar;30(6):1192. doi: 10.3390/molecules30061192
Belfield SJ, Basiri H, Swapnil C, Chrysochoou G, Enoch SJ, Firman JW et al. Moving towards making (quantitative) structure-activity relationships ((Q)SARs) for toxicity-related endpoints findable, accessible, interoperable and reusable (FAIR). Altex. 2025;42(4):657-666. doi: 10.14573/altex.2411161
Smajić A, Rami I, Sosnin S, Ecker GF. Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets. Chemical Research in Toxicology. 2023 Aug 21;36(8):1300-1312. Epub 2023 Jul 13. doi: 10.1021/acs.chemrestox.3c00042
Sanz F, Pognan F, Steger-Hartmann T, Díaz C, Asakura S, Amberg A et al. eTRANSAFE: data science to empower translational safety assessment. Nature Reviews. Drug Discovery. 2023 Aug;22(8):605-606. doi: 10.1038/d41573-023-00099-5
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