OpenAI connected GPT-5.4 to an automated lab and more than doubled yields on a stubborn medicinal chemistry reaction

AI chemist improves a challenging reaction in medicinal chemistry

OpenAI connected GPT-5.4 to Molecule.one's automated Maria lab and gave it an open-ended goal: improve a challenging reaction class. The model zeroed in on Chan–Lam coupling of primary sulfonamides—a high-value but low-yield substrate class—and proposed TEMPO as a mild oxidant. Across 10,080 reactions in two experiment cycles, yields improved for 88% of boronic acids and 83% of sulfonamides tested. Mean yield rose from 16.6% to 25.2%, and the share of reactions above 30% yield jumped from 15.6% to 37.5%. Bench-scale replication by human chemists confirmed the micro-liter results: 11 of 14 substrate pairs showed higher yields, most more than doubled. Sulfonamides appear in oncology, antimicrobial, and diuretic drugs, so a more reliable coupling route could widen what medicinal chemists can practically make. Humans stayed in the loop throughout—steering proposals, grading outputs, and validating the final finding.

Why it matters: OpenAI plugged GPT-5.4 into an automated lab; the model independently chose the substrate, proposed TEMPO, and hit 88% yield — a solid agent-meets-hard-science case. Capped at 78 because coupling chemistry is niche for most AI readers and the OpenAI blog carries inherent promo...