ArticleChemical science2020
Spectral deep learning for prediction and prospective validation of functional groups.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed, 120 citations in OpenAlex.
- AI-EXAFS: Physics-Informed Prediction of EXAFS from Atomic Structure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Phase-aware adaptation for functional group recognition from condensed-phase infrared spectra.RSC advances · 2026Article
- Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra.ACS omega · 2026Article
- Functional Group Identification from Infrared Spectra via a Dual-Head Convolutional Network.ACS omega · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning.ACS omega · 2025Article
- Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.Journal of chemical information and modeling · 2025Article
- Setting new benchmarks in AI-driven infrared structure elucidation.Digital discovery · 2025Article
- Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data.ACS omega · 2025Article
- Machine Learning-based Classification for the Prioritization of Potentially Hazardous Chemicals with Structural Alerts in Nontarget Screening.Environmental science & technology · 2025Article
- Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data.Journal of cheminformatics · 2025Article
- Leveraging infrared spectroscopy for automated structure elucidation.Communications chemistry · 2024Article
- Deductive machine learning models for product identification.Chemical science · 2024Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum.Sensors (Basel, Switzerland) · 2024Article
- Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants.Pharmaceutical research · 2024Article
- Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks.Analytical chemistry · 2024Article
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
- Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip.iScience · 2023Article
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 at 2 institutions in 2 countries.
Funding
Abstract
State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection.
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.