Synthesis and Structure Elucidation of Natural Products

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.

There are four broad areas where this research will have an impact:
1. Advancing scientific knowledge through the exploration of new methods for organic synthesis. Whilst the main focus of the proposal involves taking reactions we have developed and applying them on the automated platform (which frequently involves making significant changes or developing additional new protocols), we are also developing new methodologies too which will lead to innovations in the field of chemical synthesis. For example, by carrying out the known method of titrating BuLi on the Automated Platform we were able to discover how much active BuLi was lost in transfers and so we were able to more accurately add the required amount. This example highlights how automation can stimulate innovation and advance the field.
2. Providing well-trained scientists who have expertise in synthesis in the broadest terms, asymmetric synthesis, physical organic chemistry, spectroscopy and, of course, automation. The cohort of highly skilled and accomplished researchers who will be involved in this research programme will also be trained in managing research, leadership and communication skills, both through pathways established in the PIs laboratory and by the University. These researchers will support the needs of both the academic and industrial sectors. In particular, the UK chemical and pharmaceutical industries, who are major contributors to UK wealth, rely on this output. In the last year alone, members of our groups are pursuing both academic (Dr. Beatrice Collins, Bristol, RS URF; Dr. Alex Pulis, Lectureship, Leicester) and industrial (positions in Roche, Charles River, Pharmaron, CRUK, AstraZeneca) career paths, demonstrating our success in training the next generation of scientists.
3. Economic impact on Pharmaceutical, Agrochemical and Fine Chemical Industries. These industries are cornerstones of the UK economy. Personnel trained in challenging chemical synthesis, and state-of-the-art automation will be highly sought after and will contribute their skills to this vital sector. The new methodologies and new molecular entities created will be useful to this sector for the development of the next generation medicines or agrochemicals. Furthermore, we will assist this sector with our own experience of automation, providing them with new methods and techniques that can be employed on automation equipment.
4. The societal benefit of this research programme will be two-fold: (i) Safety. Scientists will no longer have to handle highly reactive or toxic materials when the chemistry is conducted on an automated platform. (ii) Automation will remove some of the repetitive tasks that scientists do giving them more interesting problems to deal with. (iii) Although automation is often feared by society and proclaimed to lead to major job losses, the reality is that it often creates as many jobs as it loses. Automation will enable scientists to create more analogues of molecules with desirable characteristics and this will lead to scientists spending more time analysing the data produced. Not all the chemistry will be amenable to automation and so parts of the synthesis may require human intervention. Additional time freed up by automation will enable the scientists to focus on what problem to solve and how to go about doing it. It moves the focus of the scientists from the synthesis to focusing on the problem to solve and interpreting the data.

When one asks about a particular piece of research "why should I [the general public] care", the answer here is that this work could have a transformative impact in automating an important area of organic synthesis. It is rare to be able to say that.