Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, U.K.
Innovation Centre in Digital Molecular Technologies, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Chem Rev. 2023 Mar 22;123(6):3089-3126. doi: 10.1021/acs.chemrev.2c00798. Epub 2023 Feb 23.
From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.
从合成化学家的培训开始，实验就基于教材和文献中的配方进行，这些配方能实现干净的反应结果，让科学家能够发展实践技能和一些化学直觉。这个过程通常会持续很长时间，因为新的配方是基于类似的反应方案开发的，而直觉引导的偏差则是通过从失败的实验中学习来进行的。然而，当试图了解感兴趣的化学系统时，已经表明基于模型、基于算法和小型化的高通量技术优于人类的化学直觉，并以更节省时间和材料的方式实现反应优化；本文详细介绍了这一点。由于许多合成化学家在本科教学中没有接触到这些技术，这导致了相当数量的希望优化反应但无法使用这些方法或根本不知道它们存在的科学家。这篇综述强调了现代化学反应优化的基础和前沿，以及它与过程放大的关系，因此可以作为这些技术的灵感科学家的参考，详细介绍了它们各自的一些应用。
