IP Review Winter 2019/20

3 Their use in identifying compounds that could be re-purposed to treat alternative diseases quickly and comparatively cheaply is well documented. This is especially advantageous where the compounds have already completed clinical trials. But as research scientists are beginning to find, AI systems are capable of achieving so much more. The re-purposing opportunity The potential applications of AI in drug discovery are almost endless, but one of the main areas of focus to date has been re-purposing existing drugs. Typically, this involves finding new uses for drugs that have already attained market and regulatory approvals for the treatment of a specific disease. Using AI technology to analyse existing research data, which might include information from clinical trials and other patient data, it is possible to determine whether a drug molecule will bind to other specific targets, and to find out more about how effective it might be at certain dosages or when treating new patient groups. When used in this way, AI systems can help to identify re-purposing opportunities more quickly and efficiently than would be possible using traditional scientific research methods. In fact, AI systems have the ability to provide a more definitive view of the potential of a re-purposed drug than would otherwise be possible. AI systems can determine whether a drug compound binds to multiple targets, and whether by binding to such targets the drug has the potential to treat diseases associated with one or more of the targets. Unlike research activities undertaken by humans, the analysis provided by AI systems is guaranteed to be objective as it is based on patterns derived from known data sources. Pooling data improves outcomes Of course, the flaw in this particular use of AI systems is that the analysis provided is only as good as the quality of the datasets in use. For this reason, the pharmaceutical industry is increasingly seeking to collaborate in order to pool data and use it to train algorithms through a process of machine learning. A recent initiative involving no fewer than ten pharmaceutical companies – including GSK, Johnson & Johnson and AstraZeneca - known as the MELLODDY Project, is using a novel blockchain system to store data on a secure ledger, whilst protecting the trade secrets of individual companies. There are many examples of early success in the area of re-purposed drugs. An innovative AI technology developed by Atomwise is using deep neural networks to aid drug discovery by analysing simulations of molecules in order to reduce the time research scientists need to spend synthesising and testing compounds. In a collaboration with IBM and the University of Toronto in 2015, the company utilised its AtomNet Cover Story AI proving valuable in drug discovery With the average cost of bringing a new drug to market now being $2.6 billion and only one in ten drug candidates making it to market despite successfully completing Phase I trials, it is no wonder that pharmaceutical companies have seized on the unparalleled data-processing potential of artificial intelligence (AI) systems.