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

该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相邻的立体中心所需的条件。这种新型立体中心的形成允许进入三维空间，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异构体过量值方面。为了解决这个问题，将使用机器学习技术来预测这些值。这将通过从不对称氢化反应的可用文献实例中开发数据集来实现。该数据集将用于开发预测模型的监督机器学习方法。反应筛选也将在实验室进行，以丰富数据集，并使用“反馈回路”方法将其输入机器学习模型。这个项目一半是计算，一半是实验，因此这个项目是由Ruth Webster博士负责实验研究，Matthew Grayson博士负责计算。

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.