Mass Spectrum Prediction

Mass spectrum prediction aims to computationally generate mass spectra from a molecule's structure, overcoming limitations of relying solely on experimental data. Current research heavily utilizes deep learning, particularly graph neural networks and transformer architectures, to model molecular structures and predict fragmentation patterns, often incorporating structural motifs or employing probabilistic approaches to improve accuracy and interpretability. These advancements are crucial for expanding spectral databases, enhancing compound identification in diverse fields like metabolomics and drug discovery, and improving the efficiency of mass spectrometry-based analyses.