Data-driven, efficient synthesis of metal complexes for imaging and drug discovery

点击翻译按钮获取中文摘要

Metal complexes have several important applications in sensing, imaging, catalysis and drug development. One of the attractive features of metal complexes is that their properties can be readily tuned for a given application by changing the oxidation state of the metal as well as the number and nature of the coordinated ligands. However, the synthesis of some of these complexes and the prediction of their properties is often challenging. This project will explore the use of high-throughput synthetic methods coupled with in-depth data analysis and mechanistic machine learning to optimise the synthesis of new metal complexes with tailored properties. The project will start by focusing on the synthesis of a library of platinum complexes with different optical and redox properties, which are of great interest as 'smart' anticancer agents, optical probes for imaging, and switchable chemical sensors to detect toxic species. The student working in this project will learn and apply state-of-the art synthetic chemistry and in situ characterisation techniques, linked with in-depth data analytics and machine learning inference methods to prepare novel metal complexes with uniquely tailored properties.