All Competitors

Contrastive encoder aligning NMR metabolomics to the plasma proteome, adding proteome-level disease risk signal to cohorts with no proteomics.

Infrared spectroscopy foundation model pretrained on 60 million simulated spectra, then adapted to real FTIR measurements of molecules and mixtures.

Single-cell metabolome inference from scRNA-seq, learned from spatially paired Visium and MALDI-MSI sections by multiple-instance learning.

Tandem mass spectrum prediction that builds explicit fragmentation pathways, mapping unknown spectra onto 800 million predicted PubChem spectra.

Mass spectrometry foundation model for untargeted metabolomics and lipidomics, naming and quantifying molecules with no reference library.

Physics-informed graph neural network predicting metabolite concentrations from gene expression, generalizing zero-shot to unseen metabolites.

NMR foundation model trained on 158 million simulated 1H and 13C spectra, transferring simulation-learned representations to real experimental data.

Metabolite annotation from tandem mass spectra by cross-modal retrieval, with a Tanimoto term keeping chemical neighbours close in the shared space.

Metabolomic foundation model pretrained on UK Biobank NMR metabolite profiles, reused with a frozen backbone for aging, subtyping, and disease risk.

Multimodal conversational LLM for metabolite analysis, fusing a molecular-graph GNN and molecular-image CNN with a Vicuna-13B language backbone.

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

Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.

Spectroscopy-grounded molecular foundation model that reads NMR, IR, and mass spectra as text, elucidating structures and generating 3D conformers.

Tandem mass spectrometry model that embeds MS/MS spectra and molecular graphs in one space, ranking candidate structures without a spectral library.

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

Pretrained variational autoencoder for untargeted blood metabolomics, learning metabolite representations that transfer to clinical prediction tasks.

Molecular formula identification from tandem mass spectra at 88.3% top-1 accuracy, more than 10x faster than fragmentation-tree search.

Chemical language model of the human metabolome that generates and ranks candidate structures for unidentified mass spectrometry peaks.

Metabolite-likeness scoring ranks any chemical structure by its distance from a learned hypersphere of known endogenous metabolites.