Turning Data into Knowledge - Data-Led Catalyst Optimisation

The use of organometallic catalysts is well established in industry, due in large part to their high selectivity and catalytic activity. However, the mechanism of transition metal catalytic reactions is often subject to change when applied to different substrates, which can lead to decreases in yield and selectivity. Often, catalyst optimisation will be necessary when using new substrates, which can be a time and resource consuming endeavour. As such, there is a growing drive to shift away from the search for a privileged catalyst to apply ubiquitously to an individual catalytic process, and instead towards the ability to predict the optimal conditions and catalyst design for a given set of substrates. To this end, researches in Bristol have developed a range of Ligand Knowledge Bases (LKBs); databases of ligands for organometallic catalysis, and computationally calculated descriptors that describe the steric and electronic properties of each ligand. Using Principle Component Analysis (PCA), ligands can be grouped based on similarities in their structural properties, and predictions on their catalytic properties can be made.

In collaboration with Bayer, ligands from across the chemical space will be screened for their catalytic capabilities towards a variety of substrates. This high-throughput screening approach will give information on what sections of the ligand space correlated with the best performance for a given application, and how those change as a factor of substrate. Using PCA, the features of effective catalysts for a given process can be identified from their descriptors and used as design principles for new ligands. New ligands will then be synthesised to judge the viability of this approach in the optimisation of catalyst discovery. In addition, any new ligands will be added to the LKB, and the catalytic mechanisms of effective ligands will be investigated. The potential use of mixed ligand systems will also be investigated as a method to further enhance the process of catalyst optimisation.

1. PEOPLE: We will train students with skills that are in demand across a spectrum of industries from pharma/biotech to materials, as well as in academia, law and publishing. The enhanced experience they receive - through interactive brainstorming, problem and dragons' den type business sessions - will equip them with confidence in their own abilities and fast-track their leadership skills. 100% Employment of students from the previous CDT in Chemical Synthesis is indicative of the high demand for the skills we provide, but as start-ups and SMEs become increasingly important in the healthcare, medicine and energy sectors, training in IP, entrepreneurship and commercialisation will stimulate our students to explore their own ventures. Automation and machine learning are set to transform the workplace in the next 20 years, and our students will be in the vanguard of those primed to make best use of these shifts in work patterns. Our graduates will have an open and entrepreneurial mindset, willing to seek solution to problems that cross disciplines and require non-traditional approaches to scientific challenges.

2. ECONOMY: Built on the country's long history of scientific ingenuity and creativity, the >£50bn turnover and annual trade surplus of £5 bn makes the British chemical sector one of the most important creators of wealth for the national economy. Our proposal to integrate training in chemical synthesis with emerging fields such as automation/AI/ML will ensure that the UK maintains this position of economic strength in the face of rapidly developing competition. With the field of drug development desperately looking for innovative new directions, we will disseminate, through our proposed extensive industrial stakeholders, smarter and more efficient ways of designing and implementing molecular synthesis using automation, machine learning and virtual reality interfaces. This will give the UK the chance to take a world-leading position in establishing how molecules may be made more rapidly and economically, how compound libraries may be made broader in scope and accessed more efficiently, and how processes may be optimized more quickly and to a higher standard of resilience. Chemical science underpins an estimated 21% of the economy (>£25bn sales; 6 million people), so these innovations have the potential for far-reaching transformative impact.

3. SCIENCE: The science emerging from our CDT will continue to be at the highest academic level by international standards, as judged by an outstanding publication record. Incorporating automation, machine learning, and virtual reality into the standard toolkit of chemical synthesis would initiate a fundamental change in the way molecules are made. Automated methods for making limited classes of molecules (eg peptides) have transformed related biological fields, and extending those techniques to allow a wide range of small molecules to be synthesized will stimulate not only chemistry but also related pivotal fields in the bio- and materials sciences. Synthesis of the molecular starting points is often the rate-limiting step in innovation. Removing this hurdle will allow selection of molecules according to optimal function, not ease of synthesis, and will accelerate scientific progress in many sectors.

4. SOCIETY: Health benefits will emerge from the ability of both academia and the pharmaceutical industry to generate drug targets more rapidly and innovatively. Optimisation of processes opens the way for advances in energy efficiency and resource utilization by avoiding non-renewable, environmentally damaging, or economically volatile feedstocks. The societal impact of automation will extend more widely to the freeing of time to allow more creative working and also recreational pastimes. We thus aim to be among the pioneers in a new automation-led working model, and our students will be trained to think through the broader consequences of automation for society as a whole