On October 23, 2020, McKinsey & Company (@McKinsey) published “Recalculating the Future of Drug Development with Quantum Computing.” The article featured the text of an interview with Chad Edwards, Product Lead at Cambridge Quantum Computing, and Lucas Siow, CEO and Co-Founder of biotech company Protein Cure.
Quantum computing (QC) allows computations to be performed far more quickly and efficiently than traditional computing and thus holds enormous potential for the life sciences industry. McKinsey notes that the game-changing nature of this approach has led pharma companies to invest significant sums to explore the application of quantum computing to human chemistry and biology.
In response to McKinsey’s question of what kinds of problems QC best suited to address in the pharma industry, Mr. Edwards explains that QC could transform the way we think about the simulation of solids, molecules, atoms, nuclei, and subatomic particles. Mr. Siow further explained the QC will be applicable to many of the workflows in computer-aided drug discovery. Furthermore, Mr. Edwards states “[t]here is little doubt that it has the potential to disrupt drug discovery.”
McKinsey has also written extensively about how artificial intelligence (AI) will impact the life sciences. The Mckinsey Global Institute report The Bio Revolution: Innovations transforming economies, societies, and our lives, states, “[t]he current innovation wave in biology has been propelled by a confluence of breakthroughs in the science itself, together with advances in computing, data analytics, machine learning, artificial intelligence (AI), and biological engineering that are enabling and accelerating the change.” It seems likely, if not obvious, that AI will certainly transform drug design, if it has not done so already.
The powerful combination of quantum computing and AI has a lot in store for human health over the next few decades. These technologies together have the ability to revolutionize drug discovery. And, if they have the ability, someone will find a way to make it so.
Today’s drug discovery and development process for a new chemical entity takes, on average, about $2 billion and twelve years to reach the market, and has a 90% failure rate. While the market potential for blockbuster drugs is enormous, the costs of discovery and developing new chemical entities has become prohibitive in most circumstances. This trend is opposite to the development costs of new products in most other industries, where “tech” has helped lower costs. The dynamic between costs, failure rate, and time to market, have dramatically altered the pharma and biotech industry’s landscape, so much so that changes have become urgent if we want to merely maintain the current status of healthcare worldwide--let alone improve it.
It is inevitable that new drugs and therapies created via QC and AI will be innovative enough to warrant patent protection--not just for the product, but possibly for the multiple processes and methods of synthesizing the product. Examples already seem to be appearing in the literature. In a paper published on October 20, 2020, in Cancer Cell, researchers describe DrugCell, a new AI system they created. Using DrugCell, the researchers input data about a tumor and the system returned synergistic drug combinations.
In July 2019, a US patent application (USSN 16/524,350) was filed listing DABUS as the inventor and identifying DABUS as an “artificial intelligence” that “autonomously generated” the invention. An individual named Stephen Thaler created DABUS, then DABUS created the invention. Thaler then filed as the applicant. In briefing to the USPTO, the patent applicant explained that DABUS conceived the invention and recognized its “novelty and salience.” In short, DABUS did everything necessary to be listed as an inventor with one exception — DABUS is not human. Although Mr. Thaler created DABUS, he felt he could not properly name himself as inventor.
The U.S. Patent Act does not expressly limit inventorship rights to humans, but does suggest that each inventor must have a name, and be an “individual.” No United States law explicitly prohibits protection for autonomous machine inventions. However, inventorship is restricted to “individuals” under, e.g., 35 U.S.C. §100(f) (1952) (“The term ‘inventor’ means the individual or, if a joint invention, the individuals collectively who invented or discovered the subject matter of the invention.”).
After rejection of his patent application, Mr. Thaler filed a petition with the USPTO to support his position that DABUS was the “inventor”. In its Decision on Petition the USPTO held that U.S patent law does not permit a machine to be named as an inventor in a patent application and denied the Petition. Whether the position taken by the USPTO is modified either internally, by the Courts, or through legislation, it is imperative for U.S. competitiveness that this position be changed. If our laws don’t provide patent protection for AI-generated inventions, the underlying policy of the entire patent system--encouraging investments in innovation and enhancing the dissemination of knowledge--is undermined.
While it is still being debated whether the patent system is the most efficient means for encouraging innovation, it remains the system we have and the system that has gotten the US (and the world) this far. If the U.S. wants to continue to encourage innovation, it must ensure its patent laws permit protection for the incredible innovations AI, and AI combined with QC, will inevitably develop.
Nothing disclosed in this report is intended to be considered as legal advice and is exclusively for informational purposes only. Nothing in this report is intended to be attributed to any company with which the authors are affiliated and is purely the opinions of the authors alone.
© 2020 Marenberg/Braginsky