Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation.
Youzhong LiuThomas De VijlderWout BittremieuxKris LaukensWouter HeyndrickxPublished in: Rapid communications in mass spectrometry : RCM (2021)
In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.
Keyphrases
- small molecule
- ms ms
- machine learning
- ultra high performance liquid chromatography
- tandem mass spectrometry
- deep learning
- high performance liquid chromatography
- big data
- liquid chromatography
- artificial intelligence
- simultaneous determination
- high resolution
- liquid chromatography tandem mass spectrometry
- gas chromatography
- protein protein
- solid phase extraction
- high resolution mass spectrometry
- mass spectrometry
- convolutional neural network
- electronic health record
- current status
- virtual reality
- density functional theory