Active learning assists chemical intuition identify a scalable conversion of chitin to 3-acetamido-5-acetylfuran

Abstract

The shift towards a more sustainable chemical and pharma industry led to considerable efforts on discoverying biorenewable synthons, amongst other approaches. Whereas lignocellulosic biomass has thrived as a source of furan building blocks, chitin has struggled in competing despite its abundance and being a source of sustainable nitrogen. This may be due to the difficulties in large scale production of chitin-derived furans. Here, we leverage active learning for the optimization of a multi-parameter reaction, namely the formation of 3-acetamido-5-acetylfuran. This active learning approach was able to outperform a trial-and-error optimization based on chemical intuition, yielding the desired N-rich furan in up to 70% yield from N-acetylglucosamine and in 10.5 mg g⁻¹ directly from dry shrimp shells. The reaction was scalable up to a 4.5 mmol scale, bypasses the use of undesirable toxic, high boiling point solvents and allows the reuse of the reaction media, supporting the utility of machine learning to advance green chemistry and the valorization of biomasses.