Library Synthesis in Flow Made Easier: DoE & ML

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The ability to optimise reactions conditions rapidly for possible synthetic challenge through high throughput experimentation (HTE) and automated screening cascades have long been industry standard workflows. Yet, many decisions made during the optimisation of a reaction towards a single compound ('local' synthetic yield maxima achieved) have unforeseen consequences when applied to the breadth of chemical diversity required from a given synthetic sequence when applied in a research project setting ('global' or multiple synthetic maxima required). Thus, synthetic sequences need to be re-designed from the beginning, which can lead to inefficient processes (time, cost, environmental burden etc.). Our accelerated approach aims to couple the self-optimising automated platforms for augmented experimental optimisation (e.g. SNOBFIT top left) with chemical case studies from Syngenta. Our aim is to address this 'local vs global' maxima problem for library synthesis in flow.We achieve this by determining the sensitivity of the rate, chemical yield, and selectivity of reactions using selected molecular descriptors in a principle component analysis and then through a Design of Experiment (DoE) methodology. The resulting statistical model will be used to generate initial best-guess starting points for synthetic conditions. The model will be updated regularly with additional screening data for reagents and reaction conditions generated from automated optimisations. This feedback loop between statistical models and experimental results will increase in accuracy with each iteration, leading to a predictive tool for mapping reactivity with starting materials, catalysts and solvents external to the initial model. This structure-property/reactivity relationship combined with a statistical response surfaces (DoE) -based approach will then be integrated with a digital twin of the process, enabling a holistic approach to the optimisation of the reaction class, including the development of a decision- support framework.