All Competitors

Contrastive dual-encoder model for DIA proteomics, embedding peptides and spectra in a shared space for zero-shot peptide-spectrum matching.

De novo peptide sequencing transformer that reads modified and unmodified peptides directly from tandem mass spectra without a reference database.

Spectral prediction model for modified peptides, forecasting fragment-ion intensities and retention time with zero-shot generalization to unseen PTMs.

Foundation model for tandem mass spectrometry that embeds MS/MS spectra into a learned chemical space, resolving isomers and classifying disease.

De novo peptide sequencing model that aligns tandem mass spectra with protein language model embeddings through constrained optimization.

Self-supervised transformer pretrained on millions of tandem mass spectra, giving embeddings for spectral annotation and fingerprint prediction.

Transformer model for de novo peptide sequencing that reads amino acid sequences directly from tandem mass spectra, with no protein sequence database.