ArticleAngewandte Chemie (International ed. in English)2026
Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.
Article in Angewandte Chemie (International ed. in English), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- [Research methods and technological advancements in herbal material science].Se pu = Chinese journal of chromatography · 2026Review
- End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers.Chemical science · 2026Article
- End-to-end multimodal structure elucidation from raw spectra combining contrastive learning and evolutionary algorithms.Nature communications · 2026Article
- DP5 without DFT: uncertainty-calibrated graph neural net accelerates structure confirmationChemical science · 2026Article
- A Multimodal Representation Learning Framework for Molecular Graph and NMR Spectrum Alignment.Entropy (Basel, Switzerland) · 2026Article
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
- Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.Angewandte Chemie (International ed. in English) · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Validating chemical synthesis success requires confirming the desired product using various analytical techniques. While spectroscopic data collection is increasingly automated, interpreting results remains a major bottleneck, often requiring expert input. With advances in laboratory automation and high-throughput synthesis, this challenge is expected to intensify. We introduce the MultiModalSpectralTransformer (MMST), a machine learning method that predicts chemical structures directly from diverse spectral data (NMR, IR, and MS). Trained on 4 million simulated compounds, MMST achieves 72% and 80% as top-1 and top-3 accuracy, respectively. To address out-of-distribution challenges, we implemented an active learning improvement cycle that generates molecules in similar chemical spaces, enabling the model to adapt to chemical structures beyond its original training data. We demonstrate MMST's capabilities through comprehensive benchmarking across diverse molecular weight ranges and chemical spaces. Notably, despite training solely on simulated data, MMST demonstrates good performance with experimental spectra. This research represents a significant advancement in automated structure elucidation, offering a powerful and adaptable tool that bridges the gap between simulated and real-world data.
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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.