Machine learning approaches to reaction design and optimisation

定制分子的设计和合成在制药工业中具有很高的重要性。通常使用试错法来设计这些分子，然而，这可能会变得非常耗时，并且对资源的要求很高。研究小组和公司正在寻找更快速、更有效、更可持续的定制分子设计方法。多年来，计算设计在工业界和学术界都得到了很好的应用，其中量子力学计算发挥了很大的作用。当系统变得更复杂时，特别是与传统的实验筛选方法（高通量实验）相比，这些计算可能会变得非常耗时。利用量子力学计算来探索催化剂和底物的构象是非常耗时的。如果能够在不需要多个实验屏幕的情况下优化反应，将使不同行业的化学家能够进一步专注于化学，减少实验设计的时间。这也将大大提高反应设计和优化的可持续性。该项目由阿斯利康（AstraZeneca）资助，将生产能够快速预测催化反应结果的机器学习模型，从而取代耗时的量子力学计算和广泛的实验设计。

The design and synthesis of tailored molecules is of high importance in the pharmaceutical industry. Frequently, trialand-error methods are used to design these molecules however, this can become time consuming with a highdemand on resources. Research groups and companies are searching for quicker and more efficient methods ofbespoke molecule design that are more sustainable.Computational design has been used both within industry and academia to good effect throughout the years, withquantum mechanical calculations playing a large role. These calculations can become time consuming when thesystems become more complicated - especially when compared to traditional experimental screening methods (highthroughputexperimentation). Exploring the conformations of catalysts and substrates using quantum mechanicalcalculations is time consuming. To be able to optimise reactions without the need for multiple experimental screenswill allow chemists across various industries to focus further on chemistry and less time on experimental design. Thiswill also greatly improve the sustainability surrounding reaction design and optimisation.This project, funded by AstraZeneca, will produce machine learning models that can rapidly predict the outcomes ofcatalytic reactions thus replacing the need for time-consuming quantum mechanical calculations and extensiveexperimental design.