The use of AI-enabled systems to accelerate R&D activity by efficiently collecting, structuring and processing vast quantities of data is well documented. However, its use during the Covid-19 pandemic, to aid the discovery of an effective vaccine, has arguably never been more critical.
The urgency of the global search for a vaccine to protect people from the coronavirus has heightened awareness of the use of AI in drug discovery. AI technologies are particularly effective at identifying opportunities to repurpose existing drugs by trawling large datasets, which could include information from previous clinical trials and other patient data. In this way, the power of deep learning has helped to identify a number of antiviral drugs, which are currently undergoing clinical trials for use in the treatment of Covid-19.
To further the exploration of the role of AI in drug discovery, in May this year GlaxoSmithKline opened a new AI hub in London. The pharmaceutical company has also partnered with Nvidia, a US tech company, in a bid to develop better hardware and new machine learning software. Specifically, the collaboration is focused on finding new algorithms and applying computation to the drug and vaccine discovery process.
There are many more potential applications for AI and machine learning technologies, however. A study published by Nature earlier this year by Stanford University’s Precourt Institute for Energy, MIT and the Toyota Research Institute, describes how AI is being used in battery development. The research project involved training a machine learning model on data from tests undertaken to assess the performance of lithium-ion batteries. Among its key findings, the study showcases an optimal method for fast charging a lithium-ion battery, in just 10 minutes – an invention that could be instrumental in supporting the adoption of electric vehicles. AI is also being used in the field of materials science to expedite discovery and development processes by streamlining the identification of candidates and reducing the number of experiments required to get them market-ready.
How AI is affecting IP
Intellectual property (IP) rights have a vital role to play in supporting the application of AI and machine learning technologies, encouraging continued investment in diverse areas of scientific R&D. The largest of the patent offices around the world consider many inventions involving AI algorithms and the discoveries they generate to be allowable subject matter. This means patent protection can potentially be obtained for these inventions, subject to normal eligibility requirements.
However, it is important to consider the commercial potential of any innovation fully and bear in mind that this can develop over a period of time. An AI algorithm developed to identify the optimal design for a commodity product, such as a new drill bit for example, could have much wider application in other areas of R&D, bringing considerable licensing potential further down the line. Therefore, protecting not only the commodity product, but also the algorithm used to design and develop it, can help to protect key areas of the innovation process. Developing an IP strategy that will protect innovations, whilst allowing for the fact that their commercial potential could continue to evolve, is therefore essential.
The R&D potential of AI and machine learning is only just starting to become apparent and there is much more to come. Widely recognised for its ability to crunch vast quantities of data and narrow the scope of research, AI-enabled systems are increasingly being used to define problems, optimise designs and, perhaps most excitingly, to come up with genuinely new possibilities. The pandemic has served to highlight the vital role that AI and machine learning seem set to play in protecting society and the planet in the future.
• The author Dr Harry Strange is an associate at European intellectual property firm, Withers & Rogers. He specialises in advising businesses in the field of computer science with a particular interest in AI & machine learning, cybersecurity, and distributed ledger technologies