Relay Therapeutics Stock: Pure Play on AI Drug Discovery & More Latest News Here – Up Jobs

A few years ago, we wrote an article about how computational chemistry is merging with artificial intelligence technologies to speed up drug discovery. Computational chemistry is a scientific field that uses computer simulations to help solve chemical problems. A related field is computational biology, which uses similar techniques to answer complex questions about biology. Add in machine learning algorithms, and scientists can now largely automate the process of identifying druggable disease targets and potential therapies before doing the more laborious and costly work of synthesizing small molecule drugs and conducting clinical trials.

We’ve written quite a bit about the many companies using AI for drug discovery. More recently, we’ve focused on four AI drug discovery stocks available to investors, and liked the business model of one company enough that we added it to our Nanalyze Disruptive Tech Portfolio. Somehow we missed a fifth AI drug discovery company that went public through a traditional IPO in July 2020 called Relay Therapeutics (RLAY). While we briefly profiled Relay Therapeutics in 2019 in our piece on computational technologies and drug discovery, it’s time to take a deep dive and see if we picked the right company for our portfolio.

About Relay Therapeutics Stock

Relay was founded in 2016 by four scientists, including Dr. David E. Shaw, a billionaire former hedge fund manager who pioneered the use of algorithms for securities trading. Today, Dr. Shaw holds a couple of different appointments at Columbia University and is also chief scientist of D.E. Shaw Research, which uses specially built supercomputers for drug discovery. 

The rest of the team are hardly slackers themselves. Dr. Dorothee Kern is a professor of biochemistry at Brandeis University who also co-founded MOMA Therapeutics, another Cambridge, Massachusetts company focused on drug discovery using many of the same techniques as Relay Therapeutics. Dr. Matthew Jacobson is also an academic and entrepreneur whose company Global Blood Therapeutics (GBT) is reportedly the target of a $5 billion acquisition by Pfizer for its sickle cell therapeutics. Jacobson also sits on the board of Schrödinger (SDGR), a computational chemistry company that leans heavily on machine learning for developing drugs. It also went public in 2020. Last but certainly not least is Dr. Mark Murcko out of MIT who has helped shepherd nine drugs to market, including two for the treatment of HIV while at Vertex Pharmaceuticals (VRTX), a biotech worth more than $70 billion.

Some equally quality names are attached to Relay’s investor list, including Google’s venture capital arm and the SoftBank Vision Fund. D.E. Shaw Research is both an investor and partner (more on that shortly). Altogether, the company had raised $520 million as a startup before an upsized IPO in July 2020 netted the company about $425 million. Last year, Relay issued a secondary offering of stock at a price of $26.50 and hauled in another $382 million and change. Today, the company sits on a market cap of $2.15 billion with Relay Therapeutics stock trading close to its original IPO price of $20 from two years ago. That’s despite the fact that the company has yet to bring a drug to market or realize significant revenue outside of one big pharma deal (also more on that shortly).

Relay Therapeutics Platform

All of this begs the question: What is the Relay Therapeutics platform and how does it work? Most drug development is focused on proteins, the molecular machines behind most biological processes. Conventional approaches rely on analyzing static images of protein fragments to provide insights on druggable targets. In contrast, Relay Therapeutics studies the three-dimensional motion of proteins using genomic data, computational biology, and machine learning. The company has termed and trademarked the approach as Motion-Based Drug Design, which it claims results in medicines with greater specificity and potency by analyzing the way proteins change shape and how a particular shape influences disease.

The platform itself is called Dynamo, also trademarked. It employs new experimental techniques such as cryo-electron microscopy, or Cryo-EM, which won the 2017 Nobel prize in chemistry by revealing high-resolution information about the structure of biomolecules. Combined with techniques like molecular dynamics and machine learning, Dynamo can reputedly speed up the drug discovery process.

The process breaks down into three key phases:

• Develop a mechanistic understanding of the dynamic behavior of a target protein and identify areas where a small molecule drug could potentially have an effect.
• Dynamo then identifies chemical starting points through a system of experimental and virtual screens, rapidly developing and prioritizing possibly drug candidates.
• Machine learning models do what they do best: repeat and rinse, so the process continuously improves the understanding of protein motion. 

In April 2021, Relay reinforced its platform with the $85 million acquisition of ZebiAI Therapeutics, a biotech that applies massive experimental DNA-encoded library datasets to power machine learning for drug discovery.

Relay Therapeutics Pipeline and Partnerships

So far, Relay has churned out three lead product candidates, all focused on cancer and all in early clinical stages of testing. Its most advanced drug, dubbed RLY-4008, targets a protein called FGFR2 that is often mutated in cancers. RLY-2608 is going after breast cancer. And RLY-1971 is being developed to inhibit a type of metastatic tumor in collaboration with Genentech, a venerable biotech firm that became a subsidiary of Roche back in 2009 for nearly $47 billion. The Genentech deal represents the only revenue that Relay has generated to date – about $95 million in upfront and milestone payments.

As we noted earlier, co-founder Shaw’s computational biochemistry research firm is a key collaborator. Relay relies on the firm’s Anton 2 supercomputer, as well as its proprietary algorithms and software, for computational modeling capabilities focused on analyzing protein motion. In other words, D.E. Shaw Research provides key technologies that make Dynamo dynamic. 

Should You Buy Relay Therapeutics Stock?

We recently profiled another AI drug discovery company, Exscientia (EXAI), which has a much more robust pipeline and portfolio of partnerships than Relay Therapeutics. But we wouldn’t invest in Exscientia because of its inconsistent revenues. In addition, its drug discovery platform remains unproven until the company can successfully bring a drug to market – a prospect that is years away from fruition. The same concerns apply to Relay Therapeutics stock. Moreover, Relay has only one major pharma deal versus at least three for Exscientia. We’re also concerned about the company’s reliance on Shaw’s research firm for key parts of its platform. Big-name scientists also have big egos, and Relay wouldn’t be the first company hobbled by infighting among its founders, each of whom has plenty of distractions with their other businesses and jobs.

Relay Therapeutics had about $838 million in its war chest, which the company claims is enough to keep the lights on into at least 2025. There’s no way any of its current drug candidates will be marketable by then. Where will the additional money come from to reach the finish line? Well, the Genentech deal is potentially worth up to $695 million if Relay opts to forego the profit/cost share model, but that’s a lot of milestones to trigger. Perhaps it can attract more customers to pay to use its platform, similar to Exscientia. The more likely scenario is yet another public offering that might dilute shareholder value. Relay Therapeutics stock is already 55% off of its high in 2020, compared to a 25% return on the Invesco QQQ Trust (QQQ), a popular exchange-traded fund (ETF) that tracks the Nasdaq-100 Index. The way things are looking now, we can expect more of the same well into the future.

Conclusion

AI drug discovery is one of the most exciting technologies today, illustrating the game-changing potential for machine-learning algorithms to help us stave off disease and live longer. But there’s a huge gap between potential and profit. Even the best of the bunch, Schrödinger, is struggling to grow revenues despite using a sort of hybrid software-as-a–service (SaaS) business model where customers license its drug discovery capabilities on an annual basis. 

We’d like to see Relay Therapeutics and the rest of the AI drug discovery companies figure out a way to establish more consistent revenue streams. They may be developing new therapies in nontraditional ways, but they’re still relying on staid business models that have seen many a biotech flame out over time.

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