ArticleAnalytical chemistry2024
Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks.
Article in Analytical chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.bioRxiv : the preprint server for biology · 2026Article
- Accelerating natural product discovery with linked MS-genomics and language/transformer-based models.npj antimicrobials and resistance · 2026Article
- Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics.Nature communications · 2026Article
- Review
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
- Comprehensive Curation and Harmonization of Small-Molecule MS/MS Libraries in Spectraverse.Analytical chemistry · 2026Article
- GNPS Feature-Based Molecular Networking as a Tool to Visualize Metabolic Toxicity from Drug-Drug Interactions: A Case Study with Methamphetamine and Ethanol.Analytical chemistry · 2026Article
- Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.Environment & health (Washington, D.C.) · 2025Review
- General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation.ArXiv · 2025Article
- Simulating Tandem Mass Spectra for Small Molecules using a General-Purpose Large-Language Model.bioRxiv : the preprint server for biology · 2025Article
- Refining EI-MS library search results through atomic-level insights.Communications chemistry · 2025Article
- Targeted Isolation of Prenylated Flavonoids fromMetabolites · 2025Article
- Automated annotation of complex natural products using a modular fragmentation-based structure assembly (MFSA) strategy.Science advances · 2025Article
- mineMS2: annotation of spectral libraries with exact fragmentation patterns.Journal of cheminformatics · 2025Article
- Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry.bioRxiv : the preprint server for biology · 2025Article
- FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events.Nature communications · 2025Article
- Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG.Computational and structural biotechnology journal · 2024Article
- MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.Journal of chemical information and modeling · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The accurate prediction of tandem mass spectra from molecular structures has the potential to unlock new metabolomic discoveries by augmenting the community's libraries of experimental reference standards. Cheminformatic spectrum prediction strategies use a "bond-breaking" framework to iteratively simulate mass spectrum fragmentations, but these methods are (a) slow due to the need to exhaustively and combinatorially break molecules and (b) inaccurate as they often rely upon heuristics to predict the intensity of each resulting fragment; neural network alternatives mitigate computational cost but are black-box and not inherently more accurate. We introduce a physically grounded neural approach that learns to predict each breakage event and score the most relevant subset of molecular fragments quickly and accurately. We evaluate our model by predicting spectra from both public and private standard libraries, demonstrating that our hybrid approach offers state-of-the-art prediction accuracy, improved metabolite identification from a database of candidates, and higher interpretability when compared to previous breakage methods and black-box neural networks. The grounding of our approach in physical fragmentation events shows especially great promise for elucidating natural product molecules with more complex scaffolds.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.