OpenAI and Molecule.one report a near-autonomous AI chemist that improved a stubborn coupling reaction

Summary

OpenAI and Molecule.one have announced a system that improved a reaction medicinal chemists rely on but have long struggled to run reliably. OpenAI billed the news as a “near-autonomous AI chemist.” The setup paired one of OpenAI’s frontier models, GPT-5.4, with Maria, Molecule.one’s agentic chemistry AI, and an automated high-throughput lab. The chemistry target was primary sulfonamide Chan-Lam coupling, a copper-mediated reaction used to attach aryl groups to sulfonamide motifs common in drug-like molecules. TEMPO-like mild oxidants are stable radical additives that can help manage the reaction’s redox chemistry. Across two rounds, yields improved for 88% of the boronic acids and 83% of the sulfonamides tested. Mean yield rose from 16.6% to 25.2%, and the share of reactions clearing 30% climbed from 15.6% to 37.5%. Maria ran 10,080 reactions in total. Human chemists then repeated representative reactions by hand at bench scale and saw higher yields for 11 of 14 substrate pairs, more than doubling for eight of them. The preprint frames the chemistry result as a broad high-throughput study in which stoichiometric TEMPO improved C–N bond formation while suppressing oxidative deboronation, a side reaction that destroys the boronic-acid partner before it can form the desired bond. It also reports that 4-hydroxy-TEMPO, or TEMPOL, maintained comparable performance as a lower-cost analog that is easier to remove during workup. While automated in many respects, humans were still in the loop. “Scientists using Maria wrote the prompts,” said Piotr Byrski, co-founder and CEO of Molecule.one. “GPT-5.4 proposed the research topic: primary sulfonamide Chan-Lam coupling, and hypothesis: TEMPO-like mild oxidants will help with yield and side products. Humans gave the final go-ahead on experiments, making minor corrections to experimental procedures. Maria turned selected ideas into HTE workflows and ran/analyzed them, GPT then reanalyzed the data and proposed a 2nd cycle using the above workflow. At the end, humans ran manual validation and wrote up the manuscript with results.” OpenAI researcher Ahmed El-Kishky noted that the research partnership showed “what becomes possible when frontier intelligence is paired with purpose-built scientific agents, automated laboratory infrastructure, and expert chemists.” He added that a model can “contribute across the research loop a…