The exponential growth of scientific literature presents an increasingly acute challenge across disciplines. Hundreds of thousands of new chemical reactions are reported annually, yet translating them into actionable experiments becomes an obstacle1,2. Recent applications of large language models (LLMs) have shown promise3–6, but systems that reliably work for diverse transformations across de novo compounds have remained elusive. Here we introduce MOSAIC (Multiple Optimized Specialists for AI-assisted Chemical Prediction), a computational framework that enables chemists to make use of the collective knowledge of millions of reaction protocols. MOSAIC is built on the Llama-3.1-8B-Instruct architecture7, training 2,498 specialized chemical experts in Voronoi-clustered spaces. This approach delivers reproducible and executable experimental protocols with confidence metrics for complex syntheses. With an overall 71% success rate, experimental validation demonstrates the realizations of more than 35 new compounds, spanning pharmaceuticals, materials, agrochemicals and cosmetics. Notably, MOSAIC also enables the discovery of new reaction methodologies that are absent from the expert’s training, a cornerstone for advancing chemical synthesis. This scalable model of partitioning vast domains into searchable expert regions enables a generalizable strategy for AI-assisted discovery wherever accelerating information growth outpaces efficient knowledge access and application.
