ArticleACS omega2025
Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed.
- Phase-aware adaptation for functional group recognition from condensed-phase infrared spectra.RSC advances · 2026Article
- Functional Group Identification from Infrared Spectra via a Dual-Head Convolutional Network.ACS omega · 2026Article
- SPECTRE: A Multimodal Spectral Transformer for Small Molecule Annotation.Journal of chemical information and modeling · 2026Article
- Setting new benchmarks in AI-driven infrared structure elucidation.Digital discovery · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interpreting spectral data to analyze the structure and properties of unknown chemicals requires a lot of time and effort. Herein, we developed a machine-learning model that simultaneously trains on multiple spectroscopic data to identify functional groups of compounds more accurately and quickly. An artificial neural network model trained on Fourier-transform infrared, proton nuclear magnetic resonance, and
Identifiers
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Registered trials
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