Rapid and Mild One‐Flow Synthetic Approach to Unsymmetrical Sulfamides Guided by Bayesian Optimization

• Published in: Chemistry methods 2021-11, Vol.1 (11), p.484-490
• Main Authors: Sugisawa, Naoto, Sugisawa, Hiroki, Otake, Yuma, Krems, Roman V., Nakamura, Hiroyuki, Fuse, Shinichiro
• Format: Article
• Language: English
• Subjects: Bayesian optimization; Design of experiments; flow; Gaussian process model; High temperature; Machine learning; Optimization; Organic chemistry; sulfamide; sulfuryl chloride
• ISSN: 2628-9725; 2628-9725
• DOI: 10.1002/cmtd.202100053

Bayesian optimization (BO) is regarded as an efficient approach that can identify optimal conditions using a restricted number of experiments. Despite demonstrated potential of BO, applications of BO‐based approaches in synthetic organic chemistry remain limited. Herein, we achieved the first rapid and mild (5.1 s, 20 °C) one‐flow synthesis of unsymmetrical sulfamides from inexpensive sulfuryl chloride. Undesired reactions were successfully suppressed and the risk in handling sulfuryl chloride was minimized by the use of micro‐flow technology. The reaction conditions producing ≥75 % yield were identified by a machine learning approach based on BO. It was demonstrated that BO produced the desired reaction conditions with a small number of experiments (19 and 10 experiments) in the entire search space (10,500 combinations of reaction conditions). Gaussian process (GP) models produced by BO provided the relationships between combinations of reaction parameters and outputs (RCRPO). The first rapid and mild (5.1 s, 20 °C) one‐flow synthesis of unsymmetrical sulfamides is achieved. The reaction conditions producing ≥75 % yield are identified by a machine learning approach using Bayesian optimization (BO). It is demonstrated that BO produces desired conditions with