Synthesis and Structure Elucidation of Natural Products

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In this project we will aim to automate the preparation of chemical compounds called polyketides, so that a robot can be programmed to make an entire library of polyketide compounds with little or no human intervention. Because this project will allow us to access dozens or hundreds of polyketides in a short space of time, we will also be developing very fast computational tools based on quantum mechanics and machine learning to design polyketides and then analyse them once they are made.Polyketides are a class of naturally occurring chemicals that comprise around 20% of the current top-selling drugs, including antibiotics, antifungals and anti-tumour agents. As such they are crucially important to human health and preparation of them (a process called "chemical synthesis") has been one of the major successes of the 20th century. However, the structures of these molecules can be extremely complex - and their chemical synthesis is incredibly challenging, in fact the synthesis of each polyketide is a separate, bespoke scientific investigation taking months, years or decades of work to complete. Even when they are made, their complexity makes the study of their structures and behaviours another challenging task - for example if we wish to understand their 3-dimensional structure and motion, then we often have to rely on demanding quantum chemical calculations that require days/weeks/months of high-end computing time to undertake. All of this contrasts with other important naturally occurring molecules, such as peptides (which make up the proteins in the body) or DNA. For these molecules, the chemical synthesis is now totally routine and can be fully automated - the scientist simply dials-up the compound they want and the robot can construct it from simple, readily available building blocks. We want to make the incredibly valuable polyketide class of compounds just as accessible.We have developed methodology for making the components of polyketides and now seek to automate their assembly on our newly acquired Chemspeed Automated Platform - a robotic chemical synthesis instrument. Through combining different building blocks on the robot, a diverse set of complex polyketides can be rapidly accessed, which in turn will enable biological studies to see if how the structure affects the biological activity. The downside of making so many new, complex chemical compounds, is then the bottle-neck created in designing or analysing the structures of the molecules we would like to make - in particular their three-dimensional structures. Our current state-of-the-art quantum chemical approaches to this are very slow (but incredibly accurate) and will simply not be able to keep up. So we propose to build on our recent development of an ultra-fast machine learning system that can mimic quantum chemical calculations, but in milliseconds rather than days or months. The robotic syntheses and resulting compounds that we make will allow us to develop and test more accurate quantum chemical methods and then use these to massively improve our machine learning system so that it is good enough to rapidly screen the hundreds or even thousands of potential structures that we might synthesise. With both the automated synthesis robot to make polyketides and the machine learning system that lets us design and study these compounds we ultimately aim to render polyketide synthesis as easy as peptide and DNA synthesis - revolutionising the way that these molecules are developed.