OpenAI and Molecule.one have published results showing that GPT-5.4, paired with an autonomous chemistry platform, drove a medicinal chemistry project from a blank-slate literature review all the way to a lab-validated scientific finding. The target: a notoriously stubborn reaction that drug makers have been trying to improve for decades.
The reaction in question is Chan-Lam coupling -- a copper-catalyzed method for forming carbon-nitrogen bonds that is widely used in drug synthesis. Chan-Lam coupling is useful in medicinal chemistry because it forms carbon-nitrogen bonds, which are common in medicines. However, the reaction does not work equally well for every class of molecule. In particular, coupling primary sulfonamides with boronic acids has historically produced low yields. Sulfonamides show up in drugs across oncology, antimicrobials, and diuretics, so a more reliable version of this reaction would meaningfully expand what medicinal chemists can build and test.
The System Behind the Science
This was not GPT-5.4 working alone in a chat window. The combined system paired complementary capabilities. Prompts written by scientists working with Maria AI were used with GPT-5.4 within a harness to generate and rank thousands of possible research proposals. Human chemists reviewed the small subset of proposals that ranked highest according to the system and selected four for laboratory testing.
The Maria platform combines frontier AI, micro-liter HTE (high-throughput experimentation) lab, and proprietary reaction data to power near-autonomous research for drug discovery and manufacturing. HTE is a technique where hundreds or thousands of miniaturized reactions are run in parallel at tiny volumes, enabling rapid screening of many conditions at once. Maria AI then translated selected high-level plans into detailed lab instructions, ran thousands of high-throughput experiments, analyzed the raw data, and returned structured results to GPT-5.4.
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