ArticleCommunications chemistry2024
Leveraging infrared spectroscopy for automated structure elucidation.
Article in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
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Who cites it
20 citing papers in PubMed.
- Evaluating 2D and 3D graph encoders for infrared-based organic structure verification.Digital discovery · 2026Article
- Phase-aware adaptation for functional group recognition from condensed-phase infrared spectra.RSC advances · 2026Article
- Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework.Chemical science · 2026Article
- High-throughput solid microsampling through stochastic robotic automation.Communications chemistry · 2026Article
- Automatic identification of compounds in molecular mixtures from liquid-phase infrared spectra.Chemical science · 2026Article
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra.ACS omega · 2026Article
- Structural determination of natural products at speeds and scale of high-throughput screening.Natural product reports · 2026Review
- Spectral Quantum Chemistry and Infrared Resonance Library for Data-Driven Molecular Spectroscopy.Scientific data · 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
- Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.Angewandte Chemie (International ed. in English) · 2026Article
- IR Spectroscopy: From Experimental Spectra to High-Resolution Structural Analysis by Integrating Simulations and Machine Learning.The journal of physical chemistry. B · 2025Article
- From Spectra to Structure: AI-PoweredAnalytical chemistry · 2025Article
- Setting new benchmarks in AI-driven infrared structure elucidation.Digital discovery · 2025Article
- Heuristic optimization in classification atoms in molecules using GCN via uniform simulated annealing.Scientific reports · 2025Article
- Unlocking the Potential of Machine Learning in Enhancing Quantum Chemical Calculations for Infrared Spectral Prediction.ACS omega · 2025Article
- Accurate and Efficient Structure Elucidation from Routine One-Dimensional NMR Spectra Using Multitask Machine Learning.ACS central science · 2024Article
- Deductive machine learning models for product identification.Chemical science · 2024Article
- Approaches, Strategies and Procedures for Identifying Anti-Inflammatory Drug Lead Molecules from Natural Products.Pharmaceuticals (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The application of machine learning models in chemistry has made remarkable strides in recent years. While analytical chemistry has received considerable interest from machine learning practitioners, its adoption into everyday use remains limited. Among the available analytical methods, Infrared (IR) spectroscopy stands out in terms of affordability, simplicity, and accessibility. However, its use has been limited to the identification of a selected few functional groups, as most peaks lie beyond human interpretation. We present a transformer model that enables chemists to leverage the complete information contained within an IR spectrum to directly predict the molecular structure. To cover a large chemical space, we pretrain the model using 634,585 simulated IR spectra and fine-tune it on 3,453 experimental spectra. Our approach achieves a top-1 accuracy of 44.4% and top-10 accuracy of 69.8% on compounds containing 6 to 13 heavy atoms. When solely predicting scaffolds, the model accurately predicts the top-1 scaffold in 84.5% and among the top-10 in 93.0% of cases.
Identifiers
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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.