Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…
A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). We combined metabolomics and machine learning to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, we studied 38 drugs with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. We validate the transferability of MoA predictions from PC-3 to two other cancer cell models and show that correct predictions are still possible, but at the expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, we predict that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as supported by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, our approach offers new opportunities, including the optimization of combinatorial drug applications.