Mass Spectrometry, Proteomics, & Analytical Chemistry AI
Cryo-EM
continuous heterogeneity / dynamics from cryo-EM data.
continuous heterogeneity / dynamics from cryo-EM data.
automatic atomic model building from cryo-EM density.
ML-based particle picking.
ML-based particle picking.
continuous heterogeneity / dynamics from cryo-EM data.
De novo peptide sequencing
transformer models that predict peptide sequences directly from MS/MS spectra without a database. Promising for immunopeptidomics, antibody sequencing, and metaproteomics.
transformer models that predict peptide sequences directly from MS/MS spectra without a database. Promising for immunopeptidomics, antibody sequencing, and metaproteomics.
transformer models that predict peptide sequences directly from MS/MS spectra without a database. Promising for immunopeptidomics, antibody sequencing, and metaproteomics.
transformer models that predict peptide sequences directly from MS/MS spectra without a database. Promising for immunopeptidomics, antibody sequencing, and metaproteomics.
Spectral prediction & DIA/DDA workflows
predict MS/MS fragmentation spectra for any peptide; enable spectral library generation without empirical measurement.
ML-based DIA data extraction; have largely replaced classical software.
predict MS/MS fragmentation spectra for any peptide; enable spectral library generation without empirical measurement.
ML-based DIA data extraction; have largely replaced classical software.
predict MS/MS fragmentation spectra for any peptide; enable spectral library generation without empirical measurement.
Structural MS
ML for back-exchange correction and peptide-level deconvolution.
ML for collisional cross-section prediction (CCSPred, DeepCCS).
ML for collisional cross-section prediction (CCSPred, DeepCCS).
ML scoring of crosslinks for integrative modeling.