DEEPNET-LLM: Highly accurate structure prediction of unknown metabolites from mass spectra via their molecular neighborhoods
- 1University of Connecticut
Abstract
Most molecular features detected by untargeted mass spectrometry-based metabolomics remain unannotated. This so-called 'dark matter' currently comprises more than 90% and likely holds uncharacterized bioactive chemistry. Structure prediction from fragmentation spectra remains an outstanding challenge, because a spectrum does not uniquely determine a structure and the space of candidate structures is mostly unbounded; correspondingly, existing methods rarely return the correct de novo structure as their top prediction. Here we describe De novo Ego network-Enabled Propagation and Neighborhood Extrapolation of Topology using Large Language Models (DEEPNET-LLM) reasoning approach, which uses Molecular Community Networking (MCN) to restrict the search of a query spectrum to its local neighborhood within the global metabolome, and applies AI reasoning to propagate structural information from neighbors to the query. For a collection of 900 authentic standards (2,249 spectra) that have never been publicly released to exclude possibility of data leakage, DEEPNET-LLM returned the correct structure as its top prediction for 84.7% of spectra. Accuracy was independent of precursor mass across a range over which the number of candidate structures grows by 30 orders of magnitude, and was comparable across ion polarity, adducts and in-source fragmentation. On a random subset of the public MassSpecGym benchmark, DEEPNET-LLM reached 85.9% top-1 accuracy excluding stereochemistry. We showcase the use of DEEPNET-LLM with an example of a discovery of tryptamine amidate of a bile acid in mouse gut, confirmed against a synthetic standard. The DEEPNET-LLM approach is orthogonal to existing in silico methods and can be combined with them.
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BibTeX
@misc{melnik2026deepnetllm,
title = {{DEEPNET-LLM: Highly accurate structure prediction of unknown metabolites from mass spectra via their molecular neighborhoods}},
author = {A. V. Melnik and A. M. P. Roberts and A. Semenov and D. Moradi and E. A. Coler and J. Goldstein and B. Ojha and N. J. Swift and S. Demirovic and M. W. Peczuh and T. D. Horvath and M. A. Engevik and V. Boginski and A. A. Aksenov},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.762558},
url = {https://www.biorxiv.org/content/10.64898/2026.10.28.762558v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - DEEPNET-LLM: Highly accurate structure prediction of unknown metabolites from mass spectra via their molecular neighborhoods
AU - A. V. Melnik
AU - A. M. P. Roberts
AU - A. Semenov
AU - D. Moradi
AU - E. A. Coler
AU - J. Goldstein
AU - B. Ojha
AU - N. J. Swift
AU - S. Demirovic
AU - M. W. Peczuh
AU - T. D. Horvath
AU - M. A. Engevik
AU - V. Boginski
AU - A. A. Aksenov
PY - 2026
DA - 2026/10/28
PB - bioRxiv
DO - 10.64898/2026.10.28.762558
UR - https://www.biorxiv.org/content/10.64898/2026.10.28.762558v1
AB - Most molecular features detected by untargeted mass spectrometry-based metabolomics remain unannotated. This so-called 'dark matter' currently comprises more than 90% and likely holds uncharacterized bioactive chemistry. Structure prediction from fragmentation spectra remains an outstanding challenge, because a spectrum does not uniquely determine a structure and the space of candidate structures is mostly unbounded; correspondingly, existing methods rarely return the correct de novo structure as their top prediction. Here we describe De novo Ego network-Enabled Propagation and Neighborhood Extrapolation of Topology using Large Language Models (DEEPNET-LLM) reasoning approach, which uses Molecular Community Networking (MCN) to restrict the search of a query spectrum to its local neighborhood within the global metabolome, and applies AI reasoning to propagate structural information from neighbors to the query. For a collection of 900 authentic standards (2,249 spectra) that have never been publicly released to exclude possibility of data leakage, DEEPNET-LLM returned the correct structure as its top prediction for 84.7% of spectra. Accuracy was independent of precursor mass across a range over which the number of candidate structures grows by 30 orders of magnitude, and was comparable across ion polarity, adducts and in-source fragmentation. On a random subset of the public MassSpecGym benchmark, DEEPNET-LLM reached 85.9% top-1 accuracy excluding stereochemistry. We showcase the use of DEEPNET-LLM with an example of a discovery of tryptamine amidate of a bile acid in mouse gut, confirmed against a synthetic standard. The DEEPNET-LLM approach is orthogonal to existing in silico methods and can be combined with them.
ER -