Making Claude a chemist \ Anthropic<br>Try Claude

Science<br>Making Claude a chemist<br>Jun 5, 2026

Summary: We’re working with world-class synthetic, computational, and analytical chemists to make Claude better at chemistry. In this post, we share our first work as part of this effort, in which Anthropic chemist, David Kamber, examines how Claude performs on a chemist’s most common analytical input, an NMR spectrum.

When working with molecules, chemists move between hand-drawn structures on a whiteboard, instrument readouts, database query strings, and the technical notations of patents and publications. Each of these representations encodes the same underlying chemistry, but each demands a different kind of fluency. A sketch of caffeine, for example, allows a chemist to spot its resemblance to adenosine, the body’s drowsiness signal, and predict that it keeps us alert by blocking the receptor. However, that same sketch cannot help a chemist tell it apart from other near-identical looking molecules.<br>Understanding what molecule a chemist is working with is critical. Chemistry undergirds everything from the foods and medicine we ingest to our lotions, paints, and plastics. Reroute a handful of bonds among the same atoms, and glucose becomes fructose, molecules sharing a formula but processed through entirely different metabolic pathways. Flip a molecule into its mirror image, and a sedative becomes a teratogen, as happened in the thalidomide disaster.1 Chemists’ everyday work depends on reading these signals correctly across whichever representation befits a given task.<br>Translating between these representations (chasing down a structure from a figure, reconciling an instrument readout against a proposed product, querying a database in the right notation) is time consuming and impossible to keep up with at scale—CAS, the largest chemistry registry, catalogs over 290 million disclosed substances and grows by roughly 15,000 new ones every day.<br>AI is well-positioned to take on this research burden, yet it still remains largely aspirational in the context of chemistry. Machine-learning tools have been positioned for years as transformative for retrosynthesis—the process of working backward from a target molecule to simpler precursors to plan how to build it—reaction prediction, and property estimation, but the data those tools need have been hard to come by—sparse on null-results, inconsistent in format, and locked behind paywalls at subscription journals (and in unstructured supporting information). Retrosynthesis is a case in point—capable AI tools have existed for years, but adoption is uneven, and the average academic or small-lab chemist still doesn't use them.<br>Even so, advancements in AI are finally reaching chemistry. Today’s frontier models are multimodal, and capable of explicit reasoning. They can read a chemical structure directly from a journal figure or hand sketch rather than depending on a pre-curated molecular database. And they can read the experimental detail of a methods section or supporting information in the form it is actually published. They can also show their reasoning step by step, which means a chemist can audit the outputs. None of this eliminates the data problem the field has been describing for years, but it changes which problems are tractable despite it.

Ultimately, our claim is a modest one: Claude is starting to meaningfully assist chemists with the daily translation, recall, and integration work that complements their judgment, and we plan to keep extending its helpfulness. Today we are publishing the first white paper in the effort to accelerate this work. It tackles a chemist's most common analytical input: an NMR spectrum.<br>Claude vs. ChemDraw on NMR prediction and structure elucidation

Full version can be found here

Nearly every small molecule—drug, pesticide, dye, fragrance, polymer, DNA or protein subunit, and functional inorganic or solid-state material—exists because a chemist determined its structure. Given that these molecules cannot be seen with microscopes, chemists must rely on spectral analysis, probing a molecule with light, radio waves, or magnetic fields. The way a given molecule absorbs, emits, or deflects this energy gives chemists a pattern, or spectrum, with which they can elucidate its structure.

NMR spectroscopy—one of the canonical techniques chemists rely on for this—is one of the most time-consuming steps in synthetic chemistry; for every compound, a chemist has to match each peak in the spectrum to an atom in the proposed structure by hand. For this white paper, we tested how Claude fared against the dedicated NMR software chemists rely on today. We measured three Claude models (Opus 4.7, Opus 4.6, Sonnet 4.6) against ChemDraw and MestReNova on 20 compounds drawn from synthetic chemistry preprints published after the models’ training cutoff so as to avoid selection bias. Both ChemDraw and MestReNova do forward prediction, using a drawn structure to...