Reaction Discovery Involving Digital co-Expert with a Practical Application in Atom-Economic Cycloaddition

The discovery of new chemical transformations is central to advancing modern chemistry, yet conventional approaches often require months or years of extensive experimental screening. Here, we present a machine-learning-assisted and expert-guided pipeline for reaction discovery applied to the search for atom-economic cycloaddition reactions. Candidate reactions were generated from publicly available quantum chemical data, filtered through unsupervised machine learning, and clustered to reduce redundancy. A digital co-expert then enabled rapid prioritization, after which human expertise provided final selection and experimental validation. This hybrid workflow is fully compatible with current laboratory infrastructure and addresses the most time-consuming stage of reaction discovery, accelerating the expert screening bottleneck by approximately 180-fold (from > 1200 days to 7 days). Within ∼1 week, two novel cycloaddition reactions were identified and experimentally confirmed, yielding previously undescribed products. While fully autonomous robotic platforms represent a long-term vision, their high cost and limited availability restrict immediate application. In contrast, our approach demonstrates the practicality of human-AI collaboration for reaction discovery, combining computational screening, machine learning and expert knowledge to efficiently expand the accessible chemical space.