Mass Spectrum

Mass spectrometry is a powerful analytical technique used to identify and characterize molecules by measuring their mass-to-charge ratio. Current research focuses on improving the accuracy and efficiency of mass spectrum prediction using machine learning, particularly employing graph neural networks and transformer architectures to model molecular structures and predict fragmentation patterns. These advancements are driving progress in diverse fields, including drug discovery, metabolomics, and clinical diagnostics, by enabling faster and more accurate identification of molecules in complex mixtures and facilitating the development of novel diagnostic tools. The integration of multiple spectroscopic techniques with machine learning is also a key area of development, aiming to leverage complementary information for more comprehensive molecular characterization.