Using Machine Learning to Access Challenging Hydrogenations: A combined theoretical and experimental approach

这个项目将使用机器学习方法来探索在四取代碳-碳双键上进行有效的不对称氢化反应以形成1，2-连续立体中心的必要条件。这种新型立体中心的形成允许移动到三维空间，以获得新的分子和药物。四取代碳-碳双键的不对称氢化反应具有挑战性，特别是在获得高对映体过量值方面。为了解决这个问题，将使用机器学习技术来预测这些值。这将通过从现有文献中不对称氢化反应的例子开发数据集来实现。该数据集将用于有监督的机器学习方法，以开发预测模型。实验室中还将进行反应筛选，以丰富数据集，并使用“反馈循环”方法将其输入到机器学习模型中。这个项目一半是计算的，一半是实验的，因此这个项目是与Ruth Webster博士的实验研究和Matthew Grayson博士的计算联合监督的。

This project will use machine learning methodologies to probe the conditions necessary to undertake efficient asymmetric hydrogenation reactions on tetra-substituted carbon- carbon double bonds to form 1,2- contiguous stereocentres. Formation of such novel stereocentres allows for movement into three-dimensional space for access to new molecules and pharmaceuticals. Asymmetric hydrogenations of tetra-substituted carbon-carbon double bonds are challenging particularly in obtaining high enantiomeric-excess values. To solve this problem machine learning techniques will be employed for the prediction of such values.This will be achieved through the development of a dataset from available literature examples of asymmetric hydrogenation reactions. This dataset will be used for supervised machine learning methodologies for the development of predictive models. Reaction screening will also be undertaken in the lab to enrich the dataset and feed in to the machine learning models using a 'feedback loop' approach. This project is half computational and half experimental as such this project is joint supervised with Dr Ruth Webster for experimental research and Dr Matthew Grayson for computational.