用流化学和机器学习解决合成中的可持续性挑战
Kristóf Ferentzi1,2, Viktor Farkas3, András Perczel2,3
1Hevesy György Doctoral School of Chemistry, Eötvös Loránd University, Budapest, Hungary.
Chemistry (Weinheim an der Bergstrasse, Germany)
|November 11, 2025
概括
这项研究引入了一种新的阿尼索尔/DMSO溶剂混合物,用于高效的合成,显著减少有毒废物和改善合. 这种绿色化学方法提高了固相合成的速度和纯度.
科学领域:
- 绿色化学 绿色化学
- 有机合成 有机合成
- 生物化学 生化学
背景情况:
- 目前的酸生产方法昂贵,并产生大量的有毒废物.
- 替代N,N-Dimethylformamide (DMF) 的努力取得了有限的成功.
- 需要可持续和高效的合成技术.
研究的目的:
- 开发一种更绿色,更高效的合成方法.
- 为了确定最佳的溶剂混合物,以改善合和Fmoc-cleavage.
- 为了最大限度地减少像种族化和阿斯巴尔蒂胺形成这样的副作用.
主要方法:
- 探索溶剂参数空间以确定最佳混合物.
- 测试溶剂混合物的膨胀,可溶性,合和Fmoc裂变.
- 合成具有挑战性的序列 (毒素II,JR10-mer) 以验证溶剂性能.
- 使用贝叶斯优化 (机器学习) 优化Fmoc-cleavage的优化.
- 高温固相合成. 高温固相合成.
主要成果:
- 一种安尼索尔/DMSO (17:3) 混合物被认为是合并的理想选择.
- 种族化减少到His的<2%和Cys的<1%.
- 优化了Fmoc-cleavage参数,最大限度地减少了aspartimide的形成.
- 成功合成复杂 (Aib-ACP,GLP-1,BPTI) 的高效率和速度 (12分钟/周期).
结论:
- 开发的方法为当前的合成技术提供了可持续和高效的替代方案.
- 安尼索尔/DMSO混合物和优化的参数使得高速,高纯度的生产成为可能.
- 这种方法适用于高温合成策略,并超越现有方法.
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