This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The human body and its gut microbiome produce thousands of small molecules that shape how the body functions—influencing immunity, metabolism and more. Identifying what those molecules actually are has been one of the biomedical sciences' most persistent bottlenecks.
More than 80% of compounds detected in a typical biological sample cannot be matched to any known structure using current methods. Researchers at the Boyce Thompson Institute (BTI) and Cornell University have developed a tool that begins to change that. AIMe, short for AI Molecule Explorer, uses a form of artificial intelligence called neuro-symbolic AI to predict, organize and search the mass spectra of more than 100 million known small organic molecules—effectively building a vast searchable map of chemical space that can accelerate hypothesis generation and compound identification.
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. AIMe is accessible here, and the source code will be made publicly available upon publication of the study. The preprint is available on bioRxiv.
Mass spectrometry is the workhorse for small-molecule identification 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 about a compound's structure 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.
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