Artificial intelligence evaluates chemical spectra in minutes
Artificial intelligence evaluates chemical spectra in minutes
Publish Date: 2026-06-23 00:16:00
Source Domain: www.chemeurope.com
Determining which substance has actually been produced in a test tube or flask is one of the central tasks of chemistry. Particularly in the case of complex or novel compounds, however, this can be extremely time-consuming, even for experienced specialists. A research team from Friedrich Schiller University Jena, Helmholtz-Zentrum Berlin for Materials and Energy, the Helmholtz Institute for Polymers in Energy Applications Jena and the Swiss software company Zakodium Sárl has now developed an artificial intelligence (AI) system that proposes suitable molecular structures from the raw data of spectroscopic measurements and assesses their plausibility. The system is openly accessible and has been presented in the journal Nature Communications.
Why structure elucidation is so challenging
“Anyone who synthesises a molecule must also prove its chemical structure,” says Dr Kevin Jablonka of the University of Jena. He adds: “To do this, chemists typically use analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy, infrared spectroscopy and mass spectrometry. Each of these methods provides clues about the structure, but often only to a limited extent. The many individual measurement signals therefore form a kind of chemical puzzle that must be solved correctly.”
Structure analysis is often particularly challenging for novel molecules that have never been described before, especially because measurement data obtained in practice are frequently less than ideal. “Impurities in a substance can generate their own signals or overlap with the signals of the target compound,” explains Jablonka. “This is where our system has a particular strength: for proton NMR spectra, which are routinely measured very frequently, it can cope with impurities present in real samples.”
How SECS works
“The new system, SECS,…
