Synthesis, the science of making molecules, is central to human wellbeing through its ability to produce new molecules for use as medicines and materials. Every new drug, whether an antibiotic or a cancer treatment, is based on a molecular structure designed and built using the techniques of synthesis. Synthesis is a complex activity, in which bonds between atoms are formed in a carefully choreographed way, and training to a doctoral level is needed to produce scientists with this expertise. Our proposed CDT is tailored towards training the highly creative, technologically skilled scientists essential to the pharmaceutical, biotech, agrochemical and materials sectors, and to many related areas of science which depend on novel molecules. Irrespective of the ingenuity of the synthetic chemist, synthesis is often the limiting step in the development of a new product or the advance of new molecular science. This hurdle has been overcome in some areas by automation (e.g. peptides and DNA), but the operational complexity of a typical synthetic route in, say, medicinal chemistry has hampered the wider use of the technology. Recent developments in the fields of automation, machine learning (ML), virtual reality (VR) and artificial intelligence (AI) now make possible a fundamental change in the way molecules are designed and made, and we propose in this CDT to engineer a revolution in the way that newly trained researchers approach synthetic chemistry, creating a new generation of pioneering innovators. Making use of Bristol's extensive array of automated synthetic equipment, flow reactors, peptide synthesisers, and ML Retrosynthesis Tool, students will learn and appreciate this cutting-edge technology-driven program, its potential and its limitations. Bristol has outstanding facilities, equipment and expertise to deliver this training. At its core will be a state-of-the-art research experience in our world-leading research groups, which will form the majority of the 4-year CDT training period. For the 8 months prior to choosing their project, students with complete a unique, multifaceted Technology & Automation Training Experience (TATE). They will gain hands-on experience in advanced techniques in synthesis, automation, modelling and virtual reality. In conjunction with our Dynamic Laboratory Manual (DLM), the students will also expand their experience and confidence with interactive, virtual versions of essential experimental techniques, using simulations, videos, tutorials and quizzes to allow them to learn from mistakes quickly, effectively and safely before entering the lab. In parallel, they will develop their teamworking, leadership and thinking skills through brainstorming and problemsolving sessions, some of them led by our industrial partners. Brainstorming involves the students generating ideas on outline proposals which they then present to the project leaders in a lively and engaging interactive feedback session, which invariably sees new and student-driven ideas emerge. By allowing students to become fully engaged with the projects and staff, brainstorming ensures that students take ownership of a PhD proposal from the start and develop early on a creative and collaborative atmosphere towards problem solving. TATE also provides a formal assessment mechanism, allow the students to make a fully informed choice of PhD project, and engages them in the use of the key innovative techniques of automation, machine learning and virtual reality that they will build on during their projects. We will integrate into our CDT direct interaction and training from entrepreneurs who themselves have taken scientific ideas from the lab into the market. By combining our expertise in synthesis training with new training platforms in automation, ML/AI/VR and entrepreneurship this new CDT will produce graduates better able to navigate the fast-changing chemical landscape.

Catalytic multicomponent reactions that transform the C=C bonds of alkene feedstocks into complex molecules for the interrogation of biological systems are a cornerstone of modern synthesis. The intrinsic multifaceted reactivity of C=C bonds can be unlocked by many catalytic activation modes. When combined with the structural & functional diversity inherent to the ubiquitous classes of alkene feedstock, these activation modes offer remarkable flexibility for programmable synthesis of complex architectures.1 Among many classes of these reactions, transformations that form new C-C & C-N bonds are an attractive starting point for new methodologies involving transition metal-catalyzed aminoarylation. Recently, we reported a distinct catalysis platform that enables a multicomponent coupling of alkenes, aryl-electrophiles & NaN3, providing single-step access to synthetically versatile & functionally diverse beta-arylethylamine derivatives. Driven by visible-light, two discrete Cu-catalysts orchestrate Ar-radical formation & azido-group transfer steps, which underpin an alkene azido-arylation (AAA) process. The reaction exhibits broad scope in alkene & Ar-components & the azide-anion performs a multifaceted role as both nitrogen source & in mediating the redox-neutral dual-catalysis platform via inner-sphere electron transfer. The synthetic capabilities of this anion-mediated AAA & development of its related reactions is likely to be of utility in a variety of pharmaceutically relevant & wider synthetic applications. Despite several notable advances, the vast majority of synthetic chemistry is conducted in 'one-at-a-time' batch fashion using equipment that has not, essentially, changed since urea was first synthesized by Wöhler in 1828. Most synthetic chemistry is still based on a work flow that often involves routine operations and is labour-intensive & time consuming. Over the last four years, the PI & team have established a ns-HTE platform, such that we can execute & analyse 1000s of parallel & discretely programmable reactions across a wide range of chemical reaction space. The platform is facilitated by liquid handling robots (LHRs), which enables reactions to be set up on a micro or nanomolar scale. To analyse reaction mixtures from 384 or 1536-well plates, we can choose from quantitative & semi-quantitative LC-MS, high-throughput (HT) qNMR & parallel HT-TLC. Together these techniques allow unparalleled quantification & structure determination of products on a short timescale. Together we aim to use HTE to epxlore a new type a catalysis for the synthesis of complex molecules from alkenes. The 'anion-gated dual catalysis' platform brings together three readily available building blocks in a process controlled, ultimately, by a simple anion. The products can be advnaced to functional molecules that have unexplored properties in biologial systems, providing a means to explore new chemical and biology space.

Despite the rise of biological therapies, the discovery of new and improved medicinal agents to treat disease is still dominated by small molecules. The challenges in discovering a new molecular medicine are significant indeed - typically taking about 12 years from laboratory to patient, and costing of the order of $2 bn for each new drug. As a result, the pharmaceutical industry is continually looking for new approaches to improve the efficiency and productivity of the drug discovery process. The binding of a drug to its target protein can be likened to the fitting of a key into a lock, and the design of molecular 'keys' that have the appropriate arrangements of teeth and grooves to complement the 'lock' of the protein binding site is a major challenge - particularly when one considers that the protein binding sites (and hence the molecules that need to interact with them) are generally highly complex and three-dimensional in shape. One approach to this problem, that has become increasing important over the last 15-20 years, is fragment-based drug discovery (FBDD). Here, the drug discovery process begins with fragments: very small molecules that are broadly analogous to an individual groove or tooth motif of a key. Fragments are then grown iteratively (to add more grooves and/or teeth) until promising larger and tighter-binding molecules are obtained. Although a relatively new approach, this method has already resulted in medicines that are being used clinically, for example against cancer. Despite the remarkable rise of FBDD, significant chemical challenges for the field have been identified by industry. For example, limitations in the synthetic chemistry toolkit mean that growth of fragments is much easier in some directions that others. We will therefore expand this toolkit to enable efficient the growth of fragments in many different directions. Crucially, we will demonstrate that our fragment-oriented synthesis (FOS) toolkit can drive the discovery of ligands for pharmaceutically-relevant proteins. To ensure alignment with future discovery needs, we will collaborate with a pharmaceutical company that specialises in FBDD. We will ensure that our FOS toolkit becomes embedded in different types of drug discovery organisations to maximise the impact of the work.

Chemistry is a key underpinning science for solving many global problems. The ability to make any molecule or material, in any quantity needed in a prescribed timescale, and in a sustainable way, is important for the discovery and supply of new medicines to cure diseases, agrochemicals for better crop yields/protection, as well as new electronic and smart materials to improve our daily lives. Traditionally, synthetic chemistry is performed manually in conventional glassware. This approach is becoming increasingly inadequate to keep pace with the demand for greater accuracy and reproducibility of reactions, needed to support further discovery and development, including scaling up processes for manufacturing. The future of synthetic chemistry will require the wider adoption of automated (or autonomous) reaction platforms to perform reactions, with full capture of reaction conditions and outcomes. The data generated will be valuable for the development of better reactions and better predictive tools that will facilitate faster translation to industrial applications. The chemical and pharmaceutical industry is a significant provider of jobs and creator of wealth for the UK. Data from the Chemical Industries Association (CIA) shows that the chemical industry has a total turnover of £40B, adding £14.4B of value to the UK economy every year, employs 140,000 people directly, and supports a further 0.5M jobs. The sector is highly innovation-intensive: much of its annual spend of £4B on investment in capital and R&D is based on synthetic chemistry with many SME's and CRO's establishing novel markets in Science Parks across the UK regions, particularly in the South East and North West. The demand for graduate recruits by the Chemicals and Pharmaceutical industries for the period 2015-2025 is projected to be between 50,000-77,000, driven by an aging workforce creating significant volumes of replacement jobs, augmented by the need to address skills shortages in key enabling technologies, particularly automation and data skills. This CDT will provide a new generation of molecular scientists that are conversant with the practical skills, associated data science and digital technology to acquire, analyse and utilise large data sets in their daily work. This will be achieved by incorporating cross-disciplinary skills from engineering, as well as computing, statistics, and informatics into chemistry graduate programs, which are largely lacking from existing doctoral training in synthetic chemistry. Capitalising upon significant strategic infrastructural and capital investment on cutting edge technology at Imperial College London made in recent years, this CDT also attracts very significant inputs from industrial partners, as well as Centres of Excellence in the US and Europe, to deliver a unique multi-faceted training programme to improve the skills, employability and productivity of the graduates for future academic and industrial roles.

Rational computational design plays an increasingly important role in today's society, and is widely used in, for example, the construction and automotive industries to reduce costs associated with conventional experiments. If we are to apply the same principles to the design of pharmaceutical molecules, then it is necessary to be able to predict with high accuracy which of the multitude of molecules that we can potentially synthesise in the lab actually have therapeutic benefits. Ideally, the computer program would be able to perform this function using only established laws of physics, rather than relying on data input from experimental measurements. The modelling of atoms at this fundamental level is known as first principles simulation. First principles simulations are used today by researchers in many industries, including microelectronics and renewable energy, to rapidly scan multitudes of hypothetical material compositions. Only once a set of materials matching the desired properties is discovered, does the costly process of manufacturing those materials in the lab begin. So why are the same first principles techniques not used to design new pharmaceutical molecules? The equations of quantum mechanics were written down and shown to describe the atomic-scale behaviour of materials with remarkable accuracy as early as the beginning of the twentieth century. Therefore, the answer is not a lack of physical understanding. Instead, it is largely a problem of the computational effort required to model the large numbers of atoms that are involved in interactions between a pharmaceutical molecule and its therapeutic target. There are an unimaginable number of silicon atoms in typical modern electronic devices, but importantly the homogeneity of the structures means that the bulk material can be represented by just two atoms periodically repeated in 3D, and it is a relatively straightforward problem to computationally model the properties of this simple system. In contrast, biological systems are much more complex and often we need to simulate many thousands of atoms in order to accurately predict the relationships between the molecule's structure and its function. However, due to increases in computer power and, more importantly, fundamental advances in software design, first principles approaches can now access these biological systems with precisely the same accuracy that is used to study silicon. Traditional approaches to computational drug discovery rely heavily on hundreds of model parameters that have been collected over many decades from experiments or computational analysis of small molecules. My idea is to dispense with these parameters and instead compute them directly from first principles quantum mechanical simulations of the biological therapeutic target, such as a protein that is implicated in disease. These new model parameters, rather than being generic, will be specific to the system under study and will thereby transform the accuracy of computational biomolecular modelling. The improved computational models will be used to scan hundreds of potential pharmaceutical molecules for therapeutic benefit, thus allowing us to rationally and rapidly design new therapeutic candidates. Medical researchers will be able to focus their design efforts on synthesising only the most promising molecules, thereby improving the likelihood of success in the early stages of pharmaceutical development and decreasing the cost of medicines to the patient. This concept will be put into practice in collaboration with the Northern Institute for Cancer Research at Newcastle University for the design of novel cancer therapies.