Photo: Medical professionals use technology in healthcare/iStock, elenabs
Big tech companies could continue to enter the artificial intelligence-based drug discovery field and disrupt the biopharmaceutical industry in 2024. That was the consensus of panelists at a Tuesday session on AI and machine learning at the Biotech Showcase, held in conjunction with the 42nd annual J.P. Morgan Healthcare Conference.
The JPM conference was marked by a renewed recognition of Big Tech’s foray into AI-based drug discovery, with the announcement on Sunday that Isomorphic Labs, a digital biotech company from Google parent Alphabet, had signed two major deals worth nearly $3 billion with Eli Lilly and Novartis.
“Big tech companies are coming at AI with incredible momentum,” said Beth Rogozinski, CEO of Oncostyx and panel moderator, noting that the AI boom is giving rise to the “Magnificent Seven,” a new group of mega-cap tech stocks made up of seven publicly traded U.S. companies: tech giants Amazon, Apple, Alphabet, Microsoft, Meta Platforms, Nvidia and Tesla.
Last year, the combined market capitalization of the Magnificent Seven rose by nearly 75% to a staggering $12 trillion, a testament to their collective financial strength.
“Six of the seven companies are doing AI and healthcare work,” Rogozinski told the panel. “They’re all trying to get into this space.”
But Atomwise CEO Abraham Heifetz argued that the entry of big tech companies into the biopharmaceutical industry has created a “business model mismatch,” and that the Isomorphic Labs deal, in his words, “seems like traditional tech thinking.” Heifetz argued that the industry’s “physical nature of the business supports risk models, so it’s unclear,” adding that the impact of smaller companies focused on AI-based drug discovery should not be underestimated.
Google DeepMind’s AlphaFold is the foundation of the Isomorphic Lab’s platform. The problem, according to ArrePath CTO Kurt Thorn, is that these technologies “gain instant adoption” but lose market share over time: “AlphaFold was groundbreaking when it came out, but in the next few years we’ve seen a couple of alternatives emerge.”
Thorne concluded that “it’s not clear that the market size is large enough to amortize large-scale AI platforms for drug discovery across the industry.”
Rogozinski highlighted these switching costs as a potential “barrier to entry” in moving to these drug discovery platforms as big tech companies try to persuade companies to make the switch.
Vivodyne CEO Andrei Georgescu commented that drug discovery and development is a difficult and complex process that “doesn’t depend on the size of the team or how many people behind the bench.” The key to AI success in biopharmaceuticals “lies in the generation and curation of datasets,” Georgescu said, adding that the industry is “facing a bottleneck in terms of the complexity of the data and the applicability of the data to the outcomes we want to see.”
Moonwalk Biosciences CEO Alex Aravanis added humor and perspective to Tuesday’s AI session, telling the audience that he was late arriving as a panelist because of an accident involving a Tesla self-driving car on the highway. “So, obviously they need more data,” Aravanis said.
Mark Sykes, managing director of Debiopharm Innovation Fund, told Biospace he is encouraged by the increasing use of AI and machine learning in biopharmaceuticals, but predictions for 2024 remain unclear.
“The impact of AI on drug discovery is still largely unknown,” says Sykes. “Stock market valuations of AI drug discovery companies are significantly lower than their peak prices, and most of the announced high-value deals between native AI companies and big pharma are fundamentally based on future milestone payments that are unlikely to materialize.”
Greg Slabodkin is BioSpace’s news editor. Contact him at greg.slabodkin@biospace.com. Follow him on LinkedIn.
