How AI Can Improve Drug Development Amidst the Search for a COVID-19 Cure

How AI Can Improve Drug Development Amidst the Search for a COVID-19 Cure

The use of Artificial Intelligence (AI) is surging, and one area in which its use is even more important in 2020 – the year of a global pandemic – is in health technology, or healthtech.

The use of Artificial Intelligence (AI) is surging, and one area in which its use is even more important in 2020 – the year of a global pandemic – is in health technology, or healthtech.

Berlin-based, non-profit think tank dGen has just released a new report, “AI, Privacy, and Genomics: The Next Era of Drug Design”, which looks at the issue of privacy and access to genetic data for companies using AI to speed up and improve drug design.

With articles about work on a vaccine for COVID-19 flooding newspapers every day, most people are now more aware of the typical timelines involved in creating new drugs. They certainly don’t appear overnight. As the dGen report states, “The average drug today takes 10-12 years and cost $2 billion.” However, the arrival of the new coronavirus has forced researchers to think more in terms of 12-18 months, which is like exchanging a leisurely world cruise for supersonic flight.

Enter AI, which in 2020 is only just entering drug R&D where it is being used to test and improve candidate drugs before they can be fully accredited by regulators, such as the FDA in the USA.

But there is one area of concern: genetic information is central to many AI-enabled drug discovery startups. To expand this innovation, a number of issues with using genetic data must be resolved. The dGen report lists these as:

  • ownership
  • secure storage
  • availability to multiple research parties.

In order for all of us to accept the wider use of AI in drug design, privacy concerns must be addressed using the privacy-preserving techniques in machine learning. These allow researchers to process genetic material without fully revealing its source.

While this is crucial for privacy, the techniques do not address the issue of ownership, or ways to audit the system. What the dGen report proposes is a decentralized pan-European biobank network that makes information available to researchers, but all access requests have to be logged. In that way, we all know who has looked at our genetic data. Furthermore, it would allow us as individuals to grant or deny these requests and track the use of our information.

dGen’s Top Predictions for 2030

Based on better privacy-preserving technologies and an access network, dGen’s top predictions for 2030 are:

  • Better collaboration networks will emerge
  • Genetic privacy laws will be overhauled
  • AI will become a fundamental part of drug discovery
  • Pharmaceutical giants won’t be toppled, but they won’t get out unscathed as biotech startups take the field

The Views of Industry Leaders

It is also interesting to note the responses to the dGen report from those working in AI and healthtech. The think tank interviewed industry leaders from Aidence, Gero, Alphanosos, e-Estonia, Qunatlib and Turbine amongst others.

Maxim Kholin, Gero Co-Founder said,‘We believe that AI can accelerate the drug discovery process by proper understanding of human diseases from large biomedical data. The data-driven approach should help establish the genetic determinants and molecular markers of the disease’.

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