With Molecule.one’s Maria, GPT‑5.4 found a surprising additive boosting Chan-Lam Coupling yields for over 80% of tested substrates.

OpenAI’s work in science is motivated by a simple belief: advanced AI can become a powerful partner for scientists, helping them explore more ideas, connect distant concepts, design better experiments, and accelerate discoveries that benefit humanity. We have already shared early examples of models contributing to novel results in mathematics, including work on the unit distance problem⁠, in theoretical physics, through a new result on gluon amplitudes⁠, and in biology, where GPT‑5 helped lower the cost of cell-free protein synthesis⁠ in an automated lab. We also introduced GPT‑Rosalind⁠, a purpose-built model to support life sciences research and drug discovery workflows.

This project extends that trajectory into medicinal chemistry, where progress cannot be measured by reasoning alone. A hypothesis has to work in the lab with real molecules, instruments, and experimental noise. Working with Molecule.one⁠(opens in a new window), we connected GPT‑5.4 to Maria—an agentic chemistry AI integrated with a high-throughput laboratory for autonomous research—and gave it an open-ended goal: to improve one of several important reaction classes. The system generated research proposals, designed and ran experiments, analyzed experimental data, and proposed follow-up experiments. Humans remained in the loop by designing steering and grading prompts and selecting proposals to test. They also made limited corrections to experimental plans, assisted with basic laboratory operations, and independently validated the final result.

The most promising proposal, OAI-M1-03, focused on a difficult but useful version of Chan–Lam coupling, a reaction chemists use to form carbon-nitrogen bonds. Starting from the open-ended goal of improving Chan–Lam coupling for process chemistry, GPT‑5.4 independently identified primary sulfonamides as a challenging, high-value substrate class and suggested that mild oxidants, including TEMPO, could improve the reaction.

Across two cycles of experimentation in Maria Lab that idea produced a significant improvement. Under the optimized conditions, measured yields improved for 88% of the boronic acids and 83% of the sulfonamides tested. The mean yield rose from 16.6% to 25.2%, and the share of reactions above 30% yield increased from 15.6% to 37.5%. Human chemists then repeated representative reactions at bench scale. Those experiments confirmed the microliter-scale results, showing higher yields for 11 of 14 substrate pairs, with a more than twofold increase in most cases. That matters because medicinal chemists need reactions that work not just in micro-liter screening experiments, but also in practical lab workflows used during drug discovery.

Improvements in this area of medicinal chemistry are particularly exciting because synthesis is often a major bottleneck in drug discovery: scientists can only test the molecules they can make or otherwise obtain. The sulfonamide group appears in medicines across a wide range of therapeutic areas, including anticancer drugs, antimicrobials, and diuretics, yet the Chan–Lam coupling of primary sulfonamides with boronic acids has historically given low yields. Making this form of the reaction more reliable could give medicinal chemists a broader and more practical way to produce and explore potentially useful molecules.