10038464Streamlined and Targeted Design of NOTUM Inhibitors as Colorectal Cancer DrugsClosedCollaborative R&DInnovate UKBowel 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.