ArticleScience and technology of advanced materials2020
NMR-TS: de novo molecule identification from NMR spectra.
Article in Science and technology of advanced materials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Towards automatically verifying chemical structures: the powerful combination ofChemical science · 2025Article
- Automatic Identification of Potential Cellular Metabolites for Untargeted NMR Metabolomics.NMR in biomedicine · 2025Article
- Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.Journal of chemical information and modeling · 2025Article
- Network Flow Methods for NMR-Based Compound Identification.Analytical chemistry · 2025Article
- Problems, principles and progress in computational annotation of NMR metabolomics data.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.Journal of chemical information and modeling · 2022Article
- Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures.Molecules (Basel, Switzerland) · 2022Article
- Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra.Computational and structural biotechnology journal · 2022Article
- Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule.Scientific reports · 2021Article
- The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.RSC advances · 2021Review
- Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN).ACS omega · 2021Article
- Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials.International journal of molecular sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.
Indexed as
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.