Predicting and switching the site of heteroaromatic C-H functionalisation

杂环CH官能化反应在过去十年中作为经典交叉偶联反应的可行绿色替代方案引起了人们的极大兴趣，其需要较少的步骤并且产生较少的废物。尽管在这一领域取得了越来越多的进展，但区域选择性问题仍然大多没有解决。虽然一些直接杂环CH官能化确实选择性地发生在一个位置，但这通常是在反应性的“天然位点”，并且没有表现出任何真实的区域控制。在最近的出版物中，贝德福德等人证明，可以通过切换反应进行的机制来控制这些杂芳族CH官能化反应的位点选择性。DFT被用来预测最酸性和最富电子的杂环的位置，然后这些位置分别通过碱辅助去质子化（BAD）和亲电子钯化（EP）机制，允许这两个位置之间的完全区域控制。已经证明这种策略是有效的，现在感兴趣的是将其应用于其他杂环，并进一步评估预测能力和区域化学控制。该项目将进行初步研究，使用DFT来确定其他常见杂环的酸性和富电子位点，然后将它们置于BAD或EP条件下。如果这被证明是成功的，下一步将是靶向杂环的其他位置，利用不同的先天性质来确保区域控制。一旦这些调查完成，该项目将尝试整合收集的大量DFT数据，并应用机器学习来加速计算预测。

Heterocyclic CH functionalisation reactions have gathered much interest over the last decade as viable green alternatives to classic cross-coupling reactions, requiring fewer steps and producing less waste. Despite the increasing advances in this field, the issue of regioselectivity remains mostly unsolved. Though some direct heterocyclic CH functionalisations do occur selectively at one position, this is often at the "natural site" of reactivity and does not demonstrate any real regiocontrol. In a recent publication, Bedford et al. proved that it was possible to control the site selectivity of these heteroaromatic CH functionalisation reactions by switching the mechanism the reaction proceeded by. DFT was used to predict the most acidic and electron rich positions of the heterocycle and then these positions were targeted through base assisted deprotonation (BAD) and electrophilic palladation (EP) mechanisms respectively, allowing full regiocontrol between these two positions. Having proven that this strategy is effective, it is now of interest to apply this to other heterocycles and further evaluate both the predictive capabilities and regiochemical control. This project will undertake preliminary investigations, using DFT to identify the acidic and electron rich sites of other common heterocycles and then subjecting them to the BAD or EP conditions. If this proves successful, the next step will be to target other positions of the heterocycles, exploiting different innate properties to ensure regiocontrol. Once these investigations are complete, the project will then aim to try and consolidate the vast amount of DFT data that will have been collected and apply machine learning to speed up the computational prediction.