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

Abstract

Abstract 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.