We're working with synthetic, computational, and analytical chemists to make Claude better at chemistry. In this release, Anthropic publishes its first paper in that effort: evaluating how Claude handles the most common analytical input in organic chemistry, an NMR spectrum.
Summary of the experiment
Why does this matter to you, whether you're a chemist or just curious about AI? Because a big part of day-to-day chemistry is translating between representations: a hand drawing, a SMILES, a peak table, or the text of a method. Each representation demands a different kind of fluency. A model that can read and cross-check these signals speeds everything from identifying compounds to integrating published results.
Anthropic compared three Claude models (Opus 4.7, Opus 4.6, Sonnet 4.6) against two dedicated NMR programs: ChemDraw and MestReNova. The test used 20 compounds taken from preprints on ChemRxiv published after the models' training cutoff, to avoid selection bias. The 20 compounds were arranged into four structural families, five per family, each representing a different type of NMR challenge.
