Machine learning approaches to reaction design and optimisation

量身定做的分子的设计和合成在制药工业中具有重要意义。通常，设计这些分子的方法是尝试和错误的，然而，这会变得很耗时，对资源的要求很高。研究小组和公司正在寻找更快、更高效、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良好的效果，其中量子力学计算发挥了很大作用。当系统变得更加复杂时，这些计算可能会变得耗时--特别是与传统的实验筛选方法(高吞吐量实验)相比时。使用量子力学计算探索催化剂和底物的构象是很耗时的。能够在不需要多个实验筛选的情况下优化反应，将使不同行业的化学家进一步专注于化学，并减少实验设计的时间。这也将极大地提高反应设计和优化的可持续性。这个由阿斯利康资助的项目将产生机器学习模型，可以快速预测催化反应的结果，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.