Developing a new chemical reaction usually means running hundreds of trial-and-error experiments, each one testing a slightly different catalyst. This doctoral network trains chemists to flip that process: run just a handful of experiments, build a mathematical model from the data, and let the model predict which catalyst will work best next. The problem is that most chemistry labs still work the old way—testing catalysts one by one without using computational predictions to guide the search. This data-driven, “looped” approach is far more efficient, but it requires a fundamentally different workflow that few researchers have been trained to use. Without that training, the method remains underdeveloped and underused. If this succeeds, the next generation of chemists will routinely combine computation and experiment to design asymmetric catalysts—molecules that produce a single mirror-image version of a drug or agrochemical. That could slash the time and cost of developing new pharmaceuticals and fine chemicals, while also revealing the underlying mechanisms of the reactions themselves. The research is primarily about changing how chemists work, not about a specific product, but that change could quietly accelerate the entire pipeline of chiral molecule synthesis.
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Data-driven methods promise to enable a highly structured approach for the development of asymmetric catalytic reactions, founded both on experimental and computational data. In this new but underdeveloped method, a small number of experiments is performed and that data is used to make a mathematical model to predict how new ligands will behave. Such a model is based on calculated or measured physical descriptors of the ligand, and correlations obtained between enantioselectivity and such descriptors also provide mechanistic insight. In an iterative ("looped") approach, the model's predictions are tested experimentally, and fed back to make an improved model, which is again tested experimentally, until satisfactory stereoselectivity and yield is obtained. Importantly, the integration and feedback of computational and experimental data during the research process is a significantly more efficient approach to developing asymmetric catalytic reactions and also provides mechanistic insight as the reaction is developed. The main research aim of this doctoral network is to develop powerful and readily applicable workflows for data-driven development of stereoselective catalysis. Using this data-driven approach requires a fundamentally different experimental workflow to developing catalytic reactions than is currently employed in most research laboratories. Since this requires a fundamental change in the way most experimental groups work, the data-driven approach is not widespread and remains underdeveloped. The next generation of chemists requires training in combined and integrated computational and experimental approaches, both in academia and industry. The main training aim of this doctoral network is to train researchers in comprehensive data-driven experimental approach for realizing challenging asymmetric catalytic methods.
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