Anthropic has published its first chemistry-focused research as part of an expanded science effort, and the headline finding is striking: a general-purpose language model with no chemistry fine-tuning is now competitive with the specialized desktop software chemists have leaned on for decades. The white paper pits Claude against ChemDraw and MestReNova on nuclear magnetic resonance spectroscopy, the workhorse technique for figuring out what molecule you actually have in a flask.

Claude for Life Sciences already exists, but chemistry has been a notably harder nut to crack. The data is messy, locked behind paywalls, and scattered across PDFs and supporting information. Anthropic's argument is that multimodal frontier models change which problems are tractable despite that data shortage, because they can read structures off a figure or sketch and reason through experimental sections in the form they were actually published.

What NMR is, and why it eats chemists' time

NMR spectroscopy 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. The technique probes a molecule with magnetic fields and radio waves, producing a row of peaks whose positions (measured in parts per million, or ppm) encode where each hydrogen and carbon atom sits in the structure.

There are two directions you can run this. Forward prediction takes a drawn structure and simulates what the spectrum should look like. Inverse prediction, also called structure elucidation, goes the other way: given an experimental spectrum, propose the molecule. Both ChemDraw and MestReNova do forward prediction, using a drawn structure to simulate what NMR spectrum will be produced. The inverse problem is much harder and has traditionally been left to the chemist.