Current Edition

IP Issues Surrounding Machine Learning and AI in the Pharmaceutical Space

When it comes to protecting technical innovations there are two broad strategies: keep it a trade secret or obtain a patent. It has long been the case that computer-implemented technologies and pharmaceutical technologies are technical fields where one or the other approach (trade secrets in the case of computers, patents in the case of pharmaceuticals) has been typically favoured. So, in the brave new field of applying advanced computational systems to the development of pharmaceuticals, how do we develop a strategy which satisfies both approaches to protecting technical innovations?

To do so, we must look at the reasoning behind the preference for each strategy and some of the problems faced by proprietors when protecting their innovations in these technical fields.

There can be little doubt that sophisticated dynamic computational systems, such as those dubbed “Artificial Intelligence” (AI) and “Machine Learning”, are being developed for use in all stages of drug design and development. Advanced computational systems are being used to reduce the costs associated with drug development, increase the number and variety of candidates for further testing and improve testing and screening of existing candidates, to name a few example applications.

Recent reports in the press would indicate that AI systems are getting reasonably powerful at developing new and potent biologically active compounds. For example, according to a report by The Verge, “AI suggested 40,000 new possible chemical weapons in just six hours”.1 In this case, researchers were exploring how artificial intelligence could be used to develop biochemical weapons. Their findings were published in the journal “Nature Machine Intelligence”.2

So, what sort of strategy should be adopted for a system that can potentially identify 40,000 biologically active compounds in six hours, and what about the biologically active compounds themselves?


The rise of the application of AI and machine learning in the fields of healthcare and drug development has inevitably led to a corresponding rise in patent applications in this crossover field. Therefore, questions regarding what and how to effectively protect developments made in improving and adapting AI and machine learning in various aspects of pharmaceutical development is being frequently raised and discussed.

Where these computational systems assist inventors in a meaningful way, it follows that there must be some technical and patentable consideration of what parameters of the systems are required for the effective use of the system. The development and suitability of such systems for drug development must, many argue, require technical and practical consideration.