ArticleCommunications chemistry2023
An end-to-end deep learning framework for translating mass spectra to de-novo molecules.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
29 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Automatic recognition of Pd ions in high-resolution mass spectra of multicomponent samples without visible fine isotope structures.Nature communications · 2026Article
- Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework.Chemical science · 2026Article
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?Journal of cheminformatics · 2026Article
- SpecTUS: Spectral Translator for Unknown Structures Annotation from EI-MS Spectra.Analytical chemistry · 2026Article
- End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers.Chemical science · 2026Article
- Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra.ACS omega · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics.Nature communications · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Learning from All Views: A Multiview Contrastive Framework for Metabolite Annotation.Analytical chemistry · 2026Article
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
- DeePFAS: Deep-Learning-Enabled Rapid Annotation of PFAS: Enhancing Nontargeted Screening through Spectral Encoding and Latent Space Analysis.Environmental science & technology · 2025Article
- Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.Environment & health (Washington, D.C.) · 2025Review
- Learning from All Views: A Multiview Contrastive Framework for Metabolite Annotation.bioRxiv : the preprint server for biology · 2025Article
- Refining EI-MS library search results through atomic-level insights.Communications chemistry · 2025Article
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- JESTR: Joint Embedding Space Technique for Ranking candidate molecules for the annotation of untargeted metabolomics data.Bioinformatics (Oxford, England) · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database.
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.