Ithaca, NY
Researchers at the Boyce Thompson Institute (BTI) and Cornell University unveiled AIMe (AI Molecule Explorer), a neuro-symbolic artificial intelligence tool that predicts and maps the mass spectra of more than 100 million known small organic molecules. The tool tackles a long-standing bottleneck in biomedical science-more than 80% of small molecules detected in a typical biological sample cannot be matched to a known structure-a gap that has stalled discoveries linking the gut microbiome, immunity, and metabolism.
The work is a collaboration between Frank Schroeder, Professor at BTI and in Cornell's Department of Chemistry and Chemical Biology, and Carla Gomes, Professor of Computing and Information Science and director of Cornell's AI for Science Institute.
A different approach to an old problem
Mass spectrometry is the workhorse for small molecule identification used in a wide range of applications from toxicology to food analysis. When a compound is analyzed, the instrument fragments it and records the masses of the resulting pieces. That pattern of fragments, the tandem mass spectrum (or MS2 spectrum), functions as a molecular fingerprint. To identify an unknown compound, researchers compare its spectrum against a reference library of spectra from known compounds or develop hypotheses as to what the structure of a compound may be based on manual analysis of the fragmentation pattern.
The problem is that experimental reference libraries remain sparse, while expert, one-by-one interpretation is labor-intensive and slow. Collectively, available libraries cover fewer than 1% of known compounds, and resolving the structure of a single unknown can take days to months of iterative analysis and experimental validation. As a result, spectra without close library matches usually remain unannotated.
AIMe takes a different approach. Rather than waiting for experimental spectra to accumulate in libraries, it predicts spectra computationally-then organizes those predictions into a searchable resource called MS2KOSMOS. AIMe generated more than 800 million predicted spectra covering essentially all known small organic molecules in PubChem, the largest publicly available chemical database. That represents roughly a thousandfold expansion of searchable chemical space relative to existing experimental libraries.
"At the core of AIMe is DeepMS2Reasoner, a model that simulates how molecules fragment inside a mass spectrometer," explained Gomes. "It builds fragmentation pathways step by step, using symbolic chemical rules to enumerate physically plausible fragmentation steps and a neural network to assign likelihoods to each step. The result is a predicted spectrum and an annotated map of how a molecule came apart-a feature that makes AIMe's outputs interpretable in chemical terms, not just computationally useful."
From mouse gut to human biology
To demonstrate what AIMe can do in practice, the researchers applied it to a comparative metabolomics dataset from mice. The experiment compared germ-free mice, animals raised without any gut microbiota, against mice with a normal complement of gut bacteria. Several thousand chemical features differed between the two groups, and most of the abundant ones could not be identified using standard methods.
The team used AIMe to query MS2KOSMOS with spectra from the 111 most abundant unidentified microbiota-dependent compounds. For roughly a third, AIMe retrieved close predicted spectral matches and related structural candidates, providing chemically interpretable leads for follow-up. For the rest, AIMe mapped the unknown spectra to molecular neighborhoods-sets of structurally related compounds whose shared fragmentation patterns could inform hypotheses about what the unknowns might be.
"Two compounds in particular became a case study in what AI-guided structure elucidation can accomplish," said Schroeder. "Both produced spectra that suggested they were polyamine derivatives, a well-studied class of molecules, but the fragment patterns didn't match anything previously described. Using AIMe's output as a guide, our team assembled candidate structures combinatorially, predicted spectra for each candidate, and used the comparison to narrow the field."
For the first compound, the best candidate was a linear putrescine derivative, which was then easily verified by synthesizing an authentic standard. For the second, the predicted spectra of potential candidates consistently failed to explain two prominent peaks-until the team expanded the search to include cyclized variants.
Synthesis confirmed what the predictions suggested. The second compound turned out to be a structurally unusual macrocyclic polyamine-a ring-shaped variant unlike any previously reported from mouse or human biology. Searches of a large public mass spectrometry database subsequently found the same compound in samples of human origin, detected in 57 of 99 human fecal samples examined.
Polyamines occupy an important position in biology. They sit at the intersection of diet, the gut microbiome, and immune function-and these findings suggest that the catalog of microbiota-dependent polyamines is far less complete than scientists assumed.
Annotation at scale
The researchers also tested AIMe at repository scale, applying it to more than 7 million spectral clusters from the Global Natural Products Social Molecular Networking database, one of the largest publicly available repositories of mass spectrometry data. Prior annotation efforts had annotated roughly 416,000 of those clusters. AIMe returned putative annotations for approximately 2.69 million, using the same similarity threshold applied in that earlier effort.
AIMe is accessible at https://www.cs.cornell.edu/gomes/udiscoverit/kosmos/ and the source code will be made publicly available upon publication of the study. The preprint is available on bioRxiv (DOI: 10.64898/2026.08.05.743095).
About the Boyce Thompson Institute (BTI)
As an independent nonprofit research institute affiliated with Cornell University, our scientists are committed to advancing solutions for global food security, agricultural sustainability, and human health. Through groundbreaking research, transformative education, and rapid translation of discoveries into real-world applications, BTI bridges fundamental plant and molecular science with practical impact. Discovery inspired by plants since 1924. Learn more at BTIscience.org.
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