Integrating innovative computational and organic synthesis for efficient asymmetric catalyst discovery

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Background and objectives. Developing novel synthetic methodologies or molecules (eg, new/better catalysts) often require long, iterative, and cost-ineffective discovery and development processes. In contrast to NMR spectroscopy, mass spectrometry and other techniques used routinely, computational chemistry is rarely part of the organic chemistry toolbox. In this context, our research program focuses on the integration of synthetic organic and computational chemistry to increase the success rate in both catalyst and drug discovery. To do so, we develop and apply our platforms VIRTUAL CHEMIST (for the design of asymmetric catalysts) and FORECASTER (for the discovery of bioactive molecules). As all the software development and applications are done in our labs, the feedback loop enables the development of constantly improved software and molecules. We propose to further develop these tools and apply them to the design and synthesis of novel asymmetric catalysts and covalent enzyme inhibitors. Computational chemistry. VIRTUAL CHEMIST and FORECASTER rely on molecular mechanics (MM) for its speed which, in turn, uses QM-derived parameters, for their accuracy. With the ever-increasing computational power, some of the computations could now be done at the QM level while machine learning (ML) techniques could be exploited to further improve accuracy. Thus, we propose to incorporate more QM functionalities for improved accuracy (prediction of stereochemical outcome, catalytic activity and reactivity of drugs) as well as ML techniques to improve the MM predictions through ML optimization of the force fields currently used and guide the selection of molecules for screening. Organic/medicinal chemistry. Applications of these tools to established reactions (eg, Shi epoxidation) have started and will further demonstrate the accuracy of the computational predictions and their use in the design of asymmetric catalysts. We propose to apply the new QM and ML features to additional reactions including novel reactions with unknown mechanisms and/or no asymmetric versions. For these, we will 1) investigate the reaction mechanisms using a combination of organic and computational methods, 2) design/discover catalysts leading to improved stereoselectivity and 3) synthesize and test these catalysts. Similarly, while our covalent docking program has been used to develop very potent covalent inhibitors, their reactivity will be refined using a combination of computational prediction, synthesis and testing. All of these wet-lab experiments will not only produce novel molecules but provide information to the software developers for further improvements. Impact. This research program will illustrate the benefit of integrated computational/experimental chemistry and the paradigm shift for organic chemists to integrate computational tools in their toolbox. HQP will be trained on highly demanded techniques such as QM and ML as well as advanced organic synthesis.