Liquid chromatography–tandem mass spectrometry (LC-MS2) provides high-throughput screening of molecules with a large number of features. But these features are difficult to associate with specific molecular structures of each sample. To improve structure prediction from these features, Bach et al. propose a machine learning model trained to also take into account stereochemistry to combine the different kinds of features provided by LC-MS2.
- Eric Bach
- Emma L. Schymanski
- Juho Rousu
