Objective
Untargeted metabolomics is central to biology and medicine, yet a large fraction of mass-spectrometric (MS) experimental signals remains unannotated. Current identification hinges on comparing experimental properties—retention time (RT) or migration 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-instrument and cross-laboratory generalisation and achieving higher accuracy on task-specific predictions even when only small annotated datasets are available.
On top of the pre-trained backbone, we will build task-specific heads for predicting RT, MT, CCS, fragmentation spectra (MS/MS), and adduct formation. This functionality will be integrated into the CEU Mass Mediator metabolite-annotation platform to facilitate immediate uptake by the metabolomics community.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
28040 Madrid
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.