Machine learning approaches to reaction design and optimisation

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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.