Tandem Mass

Tandem mass spectrometry (MS/MS) and related computational modeling are crucial for analyzing molecular structures and improving data privacy. Current research focuses on developing advanced algorithms, such as graph transformers and diffusion models, to predict MS/MS spectra more accurately and to selectively suppress sensitive attributes in large datasets while preserving data utility. These efforts leverage machine learning techniques, including adversarial training and contrastive learning, to achieve improved performance in both spectrum prediction and privacy-preserving data transformations. The advancements in this field have significant implications for various applications, ranging from drug discovery and materials science to protecting sensitive information in large datasets.