Making Claude a Chemist

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.

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.

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.