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.

Cite: Kolomoets N.I. , Boiko D.A. , Romashov L.V. , Kozlov K.S. , Gordeev E.G. , Galushko A.S. , Ananikov V.P.
Reaction Discovery Involving Digital co‐Expert with a Practical Application in Atom‐Economic Cycloaddition
Angewandte Chemie International Edition. 2026. e23905 . DOI: 10.1002/anie.202523905 WOS Scopus OpenAlex

Dates:

Submitted:	Oct 30, 2025
Published print:	Feb 1, 2026

Identifiers:

Web of science:	WOS:001677163300001
Scopus:	2-s2.0-105029011052
OpenAlex:	W7127040688

Citing:

DB	Citing
OpenAlex	Нет цитирований
Scopus	Нет цитирований

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