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How Artificial Intelligence is Revolutionizing Drug Discovery

By Matthew Chun

In recent months, generative artificial intelligence (AI) has taken the world by storm. AI systems like ChatGPT and Stable Diffusion have captured the imagination of the masses with their impressive and sometimes controversial ability to generate human-like text and artwork. However, it may come as a surprise to some that — in addition to writing Twitter threads and dating app messages — AI is also well underway in revolutionizing the discovery of life-saving drugs.

Milestones in AI-Enabled Drug Discovery

Far from being a distant sci-fi future, AI-enabled drug discovery is already here. A non-exhaustive list of historic milestones in the field includes the following achievements:

According to Boston Consulting Group, as of March 2022, “biotech companies using an AI-first approach [had] more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials.” But how exactly is AI being used to accomplish these milestones, and why does it matter?

How AI Is Being Used

Traditional drug discovery is a notoriously time consuming and expensive process, with pre-clinical stages typically taking three to six years and costing hundreds of millions to billions of dollars. However, a host of AI tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry.

As AI systems continue to improve, the idea of fully automated end-to-end drug discovery appears less and less to be matter of if, but of when.

A Growing Industry

The excitement for AI-enabled drug discovery extends beyond just scientists, with investors taking notice as well. According to Morgan Stanley, even “modest improvements in early-stage drug development success rates enabled by the use of artificial intelligence and machine learning” could result in an additional 50 novel therapies over a 10-year period, representing a more than $50 billion opportunity. Others appear to agree, with third-party investment in AI-enabled drug discovery more than doubling annually for five consecutive years and reaching more than $5.2 billion at the end of 2021. A selection of recent financings from February 2020 to April 2021 reveals a number of players, including Schrödinger, Insitro, AbCellera, Relay Therapeutics, Atomwise, Recursion Pharmaceuticals, XtalPi, and ExScientia, who have all raised hundreds of millions of dollars to pursue their AI-driven drug discovery pipelines.

If current trends continue, it will only be a matter of time before the drugs we take are no longer designed by people, but by machines. With the promise of lower costs and shorter development timelines, AI-enabled drug discovery holds massive potential to increase the accessibility of drugs and to treat presently incurable conditions. However, it also opens the floodgates to a host of unresolved issues relating to, e.g., intellectual property rights, the risk of technological misuse, and the continued assurance of drug safety and efficacy in this new era.

Will we be ready to seize the opportunity, or will we get mired in the challenges? Our preparation as lawyers and policymakers must start now, because the future of AI-enabled drug discovery is already here.

Matthew Chun

Matthew Chun is a J.D. candidate at Harvard Law School and patent agent at Fish & Richardson P.C. He holds a DPhil in Engineering Science from the University of Oxford and a B.S. in Mechanical Engineering from the Massachusetts Institute of Technology. At Harvard Law School, Dr. Chun is Managing Editor of the Harvard Journal of Law and Technology and a Student Fellow at the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics. All opinions expressed are solely his own.

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