Kuano is a techbio startup dedicated to bringing the latest innovation and technology for drug discovery to the pharmaceutical industry. As part of their long term strategy to unlock currently undruggable enzymes they are developing advanced simulation approaches to target complex quantum systems. This Feasibility Study seeks to exploit novel algorithms to study key details of drug design targets inaccessible to alternative technologies using near term quantum computing. Kuano intends to use simulation to tackle quantum complex drug targets such as metal containing enzymes (metalloenzymes). These systems exhibit complicated long range interactions (called "correlations") that are largely inaccessible to conventional computational approaches. The company has previously developed a platform capable of selecting the most important regions of such targets to enable proof of concept simulations. This project will build on this to link the existing platform to quantum computing resources and to enable the use of new calculations that capture dynamic features particularly relevant in metalloenzymes (as well as other industrial applications). Kuano has partnered with Professor Andrew Green of University College London (UCL) and the National Quantum Computing Centre (NQCC). Professor Green is an expert in quantum computing algorithms for modelling chemical systems and will help develop and benchmark simulation approaches. NQCC will provide support in accessing and using quantum computing resources. The project output will be a new simulation engine for the Kuano drug design platform designed to unlock targets with intermediate-to-high levels of entanglement, providing a step-change for Kuano and the UK drug development industry.

Bowel cancer is a leading cause of death worldwide, with 17,000 deaths each year in the UK alone. Existing therapies suffer from the development of resistance. Kuano is looking to apply an innovative combination of quantum simulation and AI to produce a new generation of drugs targeting an enzyme (NOTUM) recently discovered to play a key role in cancer development. Like many diseases, a major cause of bowel cancer is malfunctioning or overactive enzymes - the chemical machines that accelerate chemistry within all living things. This is why 30% of existing drugs target enzymes. The Kuano platform automates a well established approach known as transition state drug (TSD) design which is specific to enzymes. To accelerate chemical reactions, enzymes bind their input chemicals incredibly tightly in a conformation known as the transition state - the speed of the reaction depending on the strength of the binding. Most drugs targeting enzymes block their function by binding to the same site as the input chemicals - if we use the transition state as a template we can design drugs that bind much more strongly. The fact that each enzyme has evolved a unique transition state also means that this template allows us to make our drugs more selective (and hence less toxic) and robust to small changes in the enzyme that might otherwise lead to resistance. Drug development is a time consuming, expensive endeavour, typically taking 15 years and around £2 billion to get one new treatment into the clinic. AI approaches are helping to reduce costs but are based on using information from previously tested molecules, limiting innovation. Traditional TSD design involves expensive and target specific experiments which take many years to tailor to a specific enzyme. By conducting experiments in a computer we can vastly reduce the cost and timelines involved in the process while also taking advantage of all of the savings associated with standard AI approaches. Even better, by using state-of-the-art simulations to understand the chemical behaviour of drug targets we are not limited to previously identified starting points and can explore all of chemical space to find the best drugs. Kuano demonstrated their platform can successfully reproduce existing TSDs and verified in the laboratory it can develop novel starting compounds for drug design. This project will allow us to produce new cancer drug candidates that would represent a huge breakthrough in medical science and, ultimately, to society.

Kuano is a company dedicated to bringing the latest innovation and technology for drug discovery to the pharmaceutical industry. Kuano's unique approach tackles common challenges in both AI-driven drug design and target driven drug discovery. This Feasibility Study seeks to exploit second generation quantum techniques to solve currently intractable problems associated with molecular simulation within the drug discovery sector. Kuano will evaluate the feasibility of a second-generation quantum technology to overcome current limitations associated with accurately modeling the behaviour of the catalysis process: specifically, extracting a description of the transition state and understanding the binding mechanisms for metalloproteins. This would enable large-scale, precise molecular simulations and support broad application in the field of 'AI in drug discovery' (as well as other industrial applications). The project output is a discovery platform that aims to unlock intermediate-to-high levels of entanglement, creating a step-change for Kuano and the UK drug development industry.

When new diseases such as COVID-19 emerge, it is not known whether existing drugs could be effective or new drugs need to be designed. The lack of information on an emerging disease makes these problems hard to solve, and it is made worse by the fact that subtle differences between diseases can have a large impact on which medicines are effective. In this project, we are using AI and computer simulations of drug and virus interactions for a known disease to find treatments for an emerging one, which shares similarities to the known disease. Additionally, when there is not enough data to decide this, our models will suggest the best potential drugs to be made and tested in order to quickly understand the different requirements of treating the new disease. Making the best choices is important as testing is both time consuming and expensive. In this project, we will focus on adapting models based on the SARS outbreak in 2002-2004 to search for drugs which might be effective against COVID-19. This approach is made possible because not only is there significant data from the related disease, but UK scientists rapidly conducted experiments showing how bits of drugs (known as 'fragments') bound to their targets within the new COVID virus. This early information provides an excellent starting point for comparing the two viruses, enabling the use of existing data to find new treatments. This work will build on Kuano's existing AI and simulation platform which was developed to design new cancer treatments. The technology developed will be applicable to future disease outbreaks as well as being able to inform more long term drug discovery projects. Successful development of our platform means that in addition to our initial goals we will make and test a small number of potential anti-COVID drugs designed by our platform. In order to bring our platform to market, we will also create a dashboard to communicate how our model integrated different data to efficiently create more diverse chemistry to potential clients.