End-to-end multimodal structure elucidation from raw spectra combining contrastive learning and evolutionary algorithms.

Elucidating molecular structures from spectroscopic data remains one of chemistry's most fundamental challenges, typically requiring extensive expert knowledge and manual interpretation of multiple analytical techniques. This is because the structure elucidation problem often has degenerate solutions for a limited set of experimental data. Existing computational approaches are limited to single spectroscopic modalities, require extensive manual preprocessing, and lack the confidence estimates and context necessary for practical application. Here we present SECS, a framework that combines contrastive learning with evolutionary algorithms to automate structure elucidation directly from raw, multimodal spectroscopic data. By aligning embeddings across NMR, infrared, and mass spectrometry, SECS mimics how experts use multiple spectroscopic lenses while providing calibrated confidence scores and relevant database context. On challenging molecular identification tasks, SECS matches expert chemist performance in head-to-head comparisons in a pilot study. The system successfully identifies incorrect structure assignments in published literature and adapts to new chemical domains without retraining by updating its reference database. Our approach demonstrates how synergistic combination of machine learning paradigms can solve analytical bottlenecks that have constrained chemical discovery.