Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.

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, ena

SUBMITTER: Priessner M 

PROVIDER: S-EPMC12790320 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature