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FoundaMet: A Foundational Model for Metabolomics

CORDIS · observation · Publication date unknown

tion time (MT), collision cross-section (CCS), and fragmentation spectra (MS/MS)—with reference libraries. These libraries are incomplete, costly to expand, and often instrument- and method-specific. Machine learning can mitigate this gap by predicting such properties directly from the compounds chemical structure, thereby enabling identification beyond existing libraries. However, existing models frequently generalise poorly across datasets and laboratories because available training sets are too small to support robust, transferable deep learning. This project will develop FoundaMet, a foundation model for metabolomics that enables reliable knowledge transfer for molecular-property prediction. FoundaMet will learn chemical structure–aware embeddings from molecular graphs via large-scale self-supervised pre-training to capture local bonding patterns and long-range substructures, followed by supervised multi-task learning that simultaneously predicts multiple molecular properties. The resulting model will provide general reasoning capabilities over metabolites, improving cross-instr

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recordType

award

status

SIGNED

region

EU

value

194074.56

unit

EUR