Artificial intelligence & machine learning

When Eli Lilly hired computational pathology expert Thomas Fuchs as the company’s first-ever chief AI officer in October, the announcement showcased pharma’s demand for a very specialized combination of skills. 

While many AI functions in pharma are outsourced, pharma and biotech companies are building up their internal AI workforces, triggering a higher need for leaders with a background straddling tech and health care.

A recent survey of about 500 industry professionals by the CRO Parexel showed it’s a pressing issue for drugmakers.

“A key insight is that 51% of biopharmaceutical leaders consider AI experts as one of the top three roles they need to fill in the next three to five years,” Keri Mattox, chief business officer at Parexel, said via email.

But recruiting experts for such specialized roles isn’t easy. At Lilly, Fuchs is tasked not only with leading the company’s strategic direction for AI initiatives in drug discovery, clinical trials, manufacturing, commercial activities and internal functions, but also with identifying, building and managing AI and machine learning solutions. 

“These are not easy skills to train, and finding candidates can be challenging due to the specialized skills required. Competition is fierce,” Mattox said.

What to look for in an AI exec

Ideal candidates for pharma and biotech AI roles have experience that combines “technological expertise with a deep understanding of drug development,” according to Mattox.

Fuchs’ resume weaves together experience in multiple industries. At Mount Sinai, he worked in digital health, AI and computational pathology, and was dean and inaugural department chair for AI and human health. He also had stints at Memorial Sloan Kettering Cancer Center, NASA's Jet Propulsion Laboratory and the California Institute of Technology.

“They should be proficient in AI and data management, with broad therapeutic and drug development knowledge — not just a siloed area,” Mattox said of candidates. “This includes the ability to critically assess AI outputs and integrate them into decision-making, which requires a strong grasp of both algorithms and clinical contexts.”

But not all candidates will have a perfect mix of skills. Usually, they’re usually stronger in one field than the other, according to Bill Holodnak, CEO and founder of the executive recruiting firm Occam Global.

“We see very few people [who] come with equal competence or equal duration in either [healthcare or tech],” he said.

Many digital experts also often start in one field and move into the other. But part of the successful hiring process is finding leaders who are equally passionate about and committed to both disciplines.

“Are they believable in their commitment to do this? Because we don't want to waste our time and our clients’ time with somebody who's doing this as a hobbyist or as just a way to scratch an intellectual itch,” Holodnak said.

Motivation is also important, especially if a candidate has “uneven” skills on paper. Someone who has more experience in the tech world, but is driven to move into health care or the life sciences because of personal experience with a certain disease would have a strong motivation to succeed in a multidisciplinary role, Holodnak said.

Other “soft skills,” are important, too, like “critical thinking, problem-solving and agility to use AI in a way that creates efficiency,” according to Mattox. 

“These skills enable the workforce to adapt quickly to changing technologies and methodologies, navigate complex challenges and envision disruptive and innovative approaches,” Mattox said. “Candidates also need to understand the importance of establishing measurable outcomes and outlining clear KPIs that demonstrate the value of AI.”

Cultural factors could also come into play including the need for tech professionals to adjust to the slower speed and more complicated regulatory hurdles of the life sciences world.

“They're used to creating a product, selling it and then selling the company,” Holodnak said. In the life sciences, “there's a longer timeline and there's much greater complexity in a product development life cycle.”

When it comes to finding talent, Mattox suggests active outreach, such as “engaging with universities and tech hubs,” as well as providing ongoing AI and other tech training for the current workforce.

“The companies actively working to develop and retain their teams are best prepared in the current environment,” she said.

AI-driven drug discovery has gotten much of the glory in pharma. 

Isomorphic Labs — which is leveraging an AI drug discovery platform based on Nobel Prize-winning AlphaFold software — pulled in the largest biotech investment haul this year to date with a $600 million fundraising round in March. 

And drug discovery is a prime focus for many of the highest valued AI companies working in pharma. Tempus AI, which has a market cap of about $9 billion, got its start leveraging molecular data for precision cancer care and has since expanded its capabilities to include drug discovery. Its services have been used by almost all of pharma’s largest public companies. 

So far, none of this momentum has resulted in a new drug approval.

Insilico Medicine is one of the frontrunners in that race with a candidate it calls the “first end-to-end generative AI-assisted drug,” that could soon move into phase 3 after garnering positive topline results late last year.

But before an AI-discovered drug proves its worth in the clinic, the technology is shining in other areas such as diagnostics and data management, according to Jessica Owens, co-founder of the healthcare-focused venture capital firm Initiate Ventures.

“I think there are going to be winners on the periphery far before we’re high-fiving about AI-discovered drugs,” she said.

These AI-driven advances may fly under the radar despite their impact because the space is currently dominated by small companies with “baby point solutions,” Owens said.

“They are all picking off one piece of the preclinical value chain,” she said.

Startups offering single service-based solutions also don’t attract as much buzz on the investment scene. 

“Historically, VCs don’t like service companies because the margins don’t work as well and they’re too hard to scale,” Owens said. “With those business models, it’s hard to get that hockey stick kind of growth.”

But that isn’t to say these startups aren’t offering strong ROI. Here’s where the technology is delivering breakthroughs throughout pharma. 

Clinical trial gains 

While shaving time off of drug discovery for a single asset can be a boon for one company, Owens sees wider value in AI technologies that offer to streamline development for the entire industry — especially in clinical trials. 

In particular, Owens pointed to Axiom Bio, a startup taking aim at predicting liver toxicities, which it says are linked to 20% to 25% of clinical drug failures. Using what it describes as the “world’s largest primary human liver dataset,” Axiom tested its AI model against assays used by Pfizer and AstraZeneca that demonstrated it was just as effective at a much lower cost. 

Ultimately, Axiom is positioning the technology to help phase out animal modeling and plans to extend its use to other organ systems including the heart and brain. 

AI solutions are also making an impact for ICON, a large research, development and commercialization firm based in Ireland. The company has implemented several AI tools, some of the proprietary, that tackle onerous aspects of clinical trial work including document storage, endpoint strategies and resource forecasting. 

In one case, ICON’s site selection tool, One Search, helped a sponsor bounce back from enrollment challenges by quickly identifying “high-performing sites” and reversing “enrollment shortfalls,” said Gerard Quinn, ICON’s vice president of IT innovation and informatics, in an email. 

Across the clinical trials industry, AI companies are working to develop solutions that automate patient enrollment and recruitment, and “match patients to trials in minutes rather than months,” Owens said. 

Diagnostics and the data deluge 

Initiate’s portfolio is also giving rise to several biotechs. The VC is currently building a startup called Tensor Bio that’s developed an AI-powered blood test for MASH that will create personalized treatment plans. 

Pinpointing effectiveness will be critical as new and high-profile MASH therapies come into play.

“This is big,” Owens said. “The first drug for MASH was recently approved, but there was a graveyard of drugs that failed before that. Some GLP-1s [could get] label expansions but none are silver bullets, so we are moving towards a combo treatment approach.” 

Another Initiate-backed company, Cornerstone AI, has developed a solution for a major behind-the-scenes problem in healthcare — cleaning large amounts of “messy” real-world data. 

“What they’re doing is not glamorous, but it’s super important,” Owens said.

Cornerstone has worked with a wide swath of companies, including biopharmas and large healthcare systems, to leverage an AI assistant that Owens said works like a “spellcheck for data” by identifying and correcting errors. 

“What would take four data scientists four months to do, can now be [done] in a week with one data scientist,” Owens said. 

Areas of potential failure — and opportunity 

AI capabilities aren’t quite ready to steal the spotlight in every area of R&D. 

Like many industry leaders, Owens argued that pharma still has a long way to go before animal modeling can be replaced by alternative testing methods based on AI, partially because the technology can’t yet replicate the intricacies of biology. 

“There’s a lot [that] AI can’t predict with respect to biology. It’s just too complex,” Owens said. “But we’ll get there.” 

And although pharma’s AI space has already been flooded with scores of companies, there are still lingering gaps in the market.

For example, there could be an opportunity for a company pulling multiple solutions under one roof and providing more end-to-end drug development services based on AI. 

“We are interested in companies linking these solutions together,” Owens said. 

And as the AI market continues to develop, the companies that ultimately succeed will also likely have a platform based on proprietary — rather than public — data.

“The way I would predict who the winners are going to be is by … looking at companies that were built on proprietary data,” Owens said. “Those are the companies that will create value over time.”

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Almost overnight, artificial intelligence moved from point solutions to transforming workflows throughout research and development. As technical capabilities advance, we will see the industry move beyond point solutions with predictive and descriptive AI to integrated processes and workflows embedded with Large Language Models and agentic AI. Agentic AI will reshape clinical research processes as we know them today, with AI-powered workflows being applied from design, to startup and throughout execution to expedite trial activities, reduce white space and improve quality.

One of the key areas of impact we will see with agentic AI is its ability to overcome the limitations of siloed data. Historically, gaining insights from varied, disparate data sources was a slow, manual endeavor that was often static and lacked comprehensiveness. The effort to synthesize insights garnered from multiple data sources into a holistic view to facilitate decision making has brought another set of challenges. Moving forward, specialized agents will be deployed to derive insights and drive actions from the data. For example, if a sponsor wanted to understand asset potential within an indication, an agentic framework could be built, including a:

• Retrieval agent to access literature, conference abstracts and other documents
• Trial search agent to capture information from trial registries
• SQL agent to engage with proprietary clinical trial data
• Specialized agent to create predictions based on related existing approvals, mechanisms of action and patient populations to name a few

The true power of an Agentic system lies in its orchestrator agent — an LLM-powered ‘command center’ that doesn’t just coordinate, but actively mobilizes a network of specialized agents. These agents operate autonomously, but within defined guardrails, leveraging real-time data and integrated tools to generate cohesive and context-rich outcomes that drive decisions and action. Agentic AI's action-oriented capabilities enable it to autonomously initiate and manage tasks, driving efficiency and ensuring timely completion of critical activities. By embedding these capabilities into clinical development workflows, sponsors can make more confident decisions on asset and indication selection and trial design to increase the probability of technical, regulatory and commercial success.

In addition to querying data for strategic business decisions, we will see changes throughout study planning and conduct that not only create efficiencies but also lead to improved data quality. It will become commonplace for clinical researchers to leverage AI assistants to enable them to quickly and easily query data, create documentation and execute tasks — with human oversight. This will lead to sponsor benefits across clinical trials, such as:

• Optimized and de-risked protocol designs
• Accelerated site activation
• Faster patient recruitment
• Enhanced oversight and agility
• Accelerated data and submission readiness

As clinical development evolves, success will hinge not just on connecting data, but on activating it. An Agentic AI platform provides intelligent collaborators that can navigate the broader clinical technology ecosystem, interfacing with platforms and executing tasks across domains. These agents don’t just surface insights; they act on them — drafting protocols, triggering workflows and accelerating trial milestones. This shift from passive data aggregation to proactive orchestration marks a fundamental leap in how R&D will operate.

The future of clinical development is being redefined by agentic AI — not through abstract and incremental innovation, but through measurable acceleration. By embedding intelligent agents into workflows, sponsors can reduce white space, compress timelines and streamline execution across clinical phases. These systems don’t just analyze —they act — initiating tasks, resolving blockers and adapting in real time. The result is a more agile, responsive R&D engine that delivers faster outcomes without compromising on quality.

The fundamentals of leadership haven’t changed much in the more than three decades Dr. Mikael Dolsten has been a prominent figure in the pharma industry, most recently as chief scientific officer and president of R&D at Pfizer.

But the world is changing rapidly, driven in part by technological advances like AI and the proliferation of data-driven approaches. Leaders can react to those changes either with their heads in the sand or their eyes wide open, said Dolsten, who capped his career with a record-breaking vaccine approval during the COVID-19 pandemic before stepping down from the Pfizer role last year.

“On a personal level, leadership is quite similar to what it’s always been,” Dolsten said. “It’s just become technically more complex — but curiosity is the real key.”

Technology has been a major driver not just of industry breakthroughs but of the slower tectonic forces underneath those advances. To understand how the currents are flowing, a leader needs to be inherently curious, Dolsten said.

“I’ve been fascinated with how much technology plays a role as a constant driver of change at an incremental innovation level,” Dolsten said. “The convergence of technology, data-driven biomedicine and curiosity on a personal level has made these three decades so amazing for me, and I hope the next two to three decades will continue to be the same thrill.”

In his post-Pfizer career, Dolsten has embarked on several paths that sate his curiosity within the wider world of biopharma, taking board seats at smaller companies that he believes can shape the future of the industry. 

Earlier this year, Dolsten joined the board of Immunai, an AI drug discovery and development company “mapping” the immune system to find new treatments for a range of diseases. The move underscores the direction he sees the industry heading in terms of technological advances, and how he believes leaders should relish the changes.

Ostriches and chameleons

When adopting new technologies or discoveries, R&D leaders can be “the bird that puts his head in the sand and doesn’t see what’s happening around it,” or they can take it all in with eyes on a swivel, “reacting constantly with a 360-degree view,” Dolsten said.

The savviest leaders will understand that a tech-friendly approach requires co-existence between humans and AI, Dolsten said.

“You have human intelligence and you have artificial intelligence, and you need to be aware of how to manage risk and biases in both,” Dolsten said. “Putting human and artificial intelligence next to one another can play such a tremendous role for pharmaceutical companies in overcoming biases or limitations in how we look at the world.”

In exploring that dynamic, Dolsten looks to his mentor and colleague Daniel Kahneman, a Nobel Prize-winning behavioral economist, who told him that “a life where you wouldn’t be able to see the color of reality in its sharpest nuances isn’t a life worth living.”

Kahneman, who died last year at the age of 90, taught Dolsten that biases in human intelligence can in fact be mastered through informed decision making. AI, when used the right way, can augment that process, Dolsten said.

The takeaway? Knowledge is the lifeblood of drug development, and advances in computing power offer it in droves.

“Whether it’s large-language models or sequencing DNA … as [long as] you’re well aware of your model, I would say artificial intelligence that analyzes large data sets is less biased than human intelligence,” Dolsten said. “That’s why I’m so fascinated when companies collaborate with Immunai and bring to the table different types of insight.”

The Immunai team, led by CEO Noam Solomon, leverages a tech and AI perspective to start speaking “the language of the future, how biology and medicine should be spoken and looked upon,” Dolsten said.

Immunai’s platform, which includes an atlas of immune cells and a development-focused immunodynamics engine, can help solve some of the challenges faced by drugmakers like AstraZeneca and Teva Pharmaceuticals, both of which have signed multi-year collaboration agreements with the company. While the pipeline is still under wraps, the companies are investigating opportunities in oncology and immunology.

The goal, Dolsten said, is to “roll up our sleeves and define the questions that therapeutic-oriented companies need to have answered.”

Personal ecosystem

With any new, wide-ranging technology like AI comes the inevitable hype, and Dolsten said it’s critical that leaders do their homework. Putting those two letters ahead of a business proposal doesn’t necessarily give it value, he said.

“My homework always starts, whether I’m filling a board seat or overseeing a partnership, with asking for references of what they’ve done recently in the area, some real case reports,” Dolsten said. 

More than anything, Dolsten said it’s important for biopharma leaders to ask questions rather than assume they have the answers, especially at a time of technological upheaval.

“I encourage people to take a step back and look at the questions that will be dominant over the next five years — you don’t need to continue with the tools or teams that make you feel comfortable,” Dolsten said. “Getting out of your comfort zone is helpful, so I welcome change, and I think it’s necessary that we have the openness and willingness to find new ways to work together.”

Dolsten’s series of board appointments since stepping down from Pfizer point to what he sees as the next wave of biopharma innovation. The former exec currently has a seat on the boards of gene therapy specialist Rocket Pharmaceuticals and oral biologics maker Orbis Medicines, and holds advisory roles that include the protein structure AI company Chai Discovery. He said more announcements will be made this year.

These steps are a way for Dolsten to explore his scientific curiosity and build a network that bridges the industry’s past and its future.

“My ambition here is to create a personal ecosystem, and I think my contribution is much larger in each of the companies because of the connectivity I bring,” Dolsten said. “I hope I can connect companies that are making breakthroughs in one area, but allow them to seamlessly communicate with tools that can be adjusted and used across different areas.”

Part of what drives this push into future technologies was the frustration Dolsten encountered in the drug development process at companies like Pfizer.

“The awareness of what it takes to make a product that brings value to patients and is also commercially successful was a continuous challenge — with some great successes and some setbacks — in my work with Pfizer,” Dolsten said. “It’s a complex playing field from launching a drug and allowing the innovation to be recognized and rewarded.”

Although Dolsten is diversifying his post-Pfizer career, he looks at AI as the great connector.

“I hope that Immunai becomes a central node for me as they try to decode the language and the letter of diseases and help this overall ecosystem be better at using that language in communication that leads to products,” Dolsten said. “The crown jewel of my career was my time at Pfizer, but I’m very curious about this next chapter and playing a role in a different way.”

After a few years of booming interest in AI platforms, the rubber has hit the road. Now, the market is watching closely to see if drugs discovered with the new tech will pan out in the clinic — and are still worth backing. 

Signs have been good so far for Insilico Medicine’s lead candidate, which it claims is the “first end-to-end generative AI-assisted drug.” The company announced positive topline results for a phase 2 study of the idiopathic pulmonary fibrosis treatment in November, which Insilico said demonstrated “encouraging clinical efficacy” by improving lung function for all 12 weeks of the trial. Insilico now plans to charge ahead into pivotal trials with the drug that was recently rechristened from ISM001-055 to rentosertib.

But results have been murkier for other AI front runners.

Recursion Pharmaceuticals revealed mid-stage data for its candidate in September that demonstrated safety but only showed a “trend” toward efficacy endpoints. 

Some industry watchdogs suggested the drug may be doomed to fail for the tough-to-treat neurovascular condition called cerebral cavernous malformation. And Recursion, which has built a diversified oncology and rare disease pipeline using an AI platform leveraging “one of the world’s largest proprietary biological and chemical datasets,” has been bolstering its clinical prospects in other directions.

Last year, Recursion picked up Exscientia, an AI drug discovery company facing its own challenges after losing a partnership with Bayer, in an acquisition worth nearly $700 million.

Have these industry shifts and uneven clinical results dulled the market’s appetite for AI drug discovery companies? 

While the COVID-19 market boom has cooled broadly across biotech, a major funding round for a Google-backed AI startup last month indicates there’s still momentum.

In fact, 71% of venture capital investments across U.S. industries in the first quarter of 2025 were tied to AI, according to PitchBook.

And in pharma, which has long been hampered by high rates of clinical setbacks, any new approach that brings even a modicum of efficacy or speed to the R&D process could trigger huge returns — a pursuit investors are still rewarding. 

Here’s a look at the biotech that raked in that major haul and at other startups that have recently won funding to chase the AI dream. 

Isomorphic Labs’ potential AI goldmine

Months after snagging the Nobel Prize in Chemistry, the CEO of Isomorphic Labs helped the company bag $600 million in its first round of external fundraising. 

The company’s AI drug discovery platform is based on AlphaFold software, which can predict protein structure, DNA, RNA and ligands that bind to targeted proteins, while creating new proteins as well. The invention was rolled out by Google DeepMind and led to a Nobel Prize for three scientists, including Demis Hassabis, CEO of both Isomorphic and Google DeepMind. 

Hassabis predicted last year that AlphaFold could help create an AI drug discovery business worth more than $100 billion — a mouth-watering proposition that investors are clearly eager to get behind.

The company’s ultimate aim is to shrink the average discovery time from five years to two. Isomorphic is also exploring emerging modalities and developing its own pipeline of candidates targeting cancer and autoimmune diseases.

"AI and machine learning have long held the promise of transforming drug discovery, yet few companies over the past decade have unlocked their full potential,” Dr. Krishna Yeshwant, managing partner at GV, one of the firms that participated in the funding round, said in a release. “After witnessing the extraordinary pace of innovation at Isomorphic Labs, we believe their pioneering approach will redefine AI-powered drug discovery.”

Thrive Capital led the $600 million funding round in March with additional boosts from GV and Alphabet.

Isomorphic also landed research collaborations with Eli Lilly and Novartis aimed at discovering small molecules for undisclosed targets last year. Combined, the research partnerships are worth up to $2.9 billion. 

Other recent AI wins

Isomorphic’s $600 million haul was the largest biotech funding round so far this year. But other AI companies have also scored significant backing.

Insilico nabbed $110 million last month in a series E round led by Value Partners Group, which has been around since the 1990s and claims to be one of the largest independent asset management firms in Asia.

The fresh capital will help Insilico refine its AI models and algorithms while it advances its pipeline of clinical candidates, the company said.

A duo of smaller upstarts also pulled in funding this year.

Simon Kohl, a scientist who worked on the second iteration of AlphaFold and co-led DeepMind’s protein design team, arrived on the biotech scene with a startup of his own in February. Called Latent Labs, the company’s AI platform is focused on “applying the latest generative techniques to design proteins from scratch,” it said in a release. Latent emerged from stealth with $50 million in funding.

Two notable names from their respective fields also unveiled an AI drug discovery startup. LinkedIn co-founder Reid Hoffman and Columbia professor Dr. Siddhartha Mukherjee, author of a landmark cancer history book “The Emperor of all Maladies,” have joined forces to launch Manas AI. The startup will leverage a Microsoft cloud computing platform and AI to design and hunt for treatments in cancer, autoimmune and rare disease, including “some previously considered unreachable,” it said in January.  

Manas AI raised $24.6 million in seed funding and will initially set its sights on breast cancer, prostate cancer and lymphoma.

Flagship Pioneering has a knack for peering into the future.

Recently, the $14 billion biotech venture capital firm that launches and runs its own companies has been leveraging that ecosystem to explore how preventive treatment can change the healthcare paradigm.

With its Preemptive Health and Medicine Initiative that began in 2022, general partner Avak Kahvejian sees “a vision for the future of how a lot of the technologies we already have at our disposal could be brought to bear not just to treat the sick but to keep us healthy longer or prevent sickness from getting worse.”

The official launch of the AI-based preventive medicine biotech Etiome is the cornerstone of that initiative, said Kahvejian, who will lead the company as founding CEO from within Flagship. The unveiling comes after several years of research into Etiome’s temporal biodynamics platform, which will get a $50 million boost from the parent VC.

“Etiome’s intent was to look at a range of chronic diseases where people progress over time into a more and more severe situation, and to apply the technologies of single-cell transcriptomics and AI with digital health records to essentially tease out the molecular and cellular journey of the disease from the patient journey,” Kahvejian said.

But how do you track a disease’s progress over decades to aid drug development? That’s where AI comes in.

“One of the key challenges in this arena is, how are you going to follow people for 20 or 30 years to see what predisposed them to a disease? What are the early signs? How sick do they get?” Kahvejian said. “A lot of pharma has avoided that whole arena because of that mindset, and they focus on sick care.”

To get over that hurdle, Etiome conducts cross-sectional studies of a broad set of patients from early-, middle- and late-stage disease, and “stitches” them together at a phenotypic and molecular level to create a single snapshot over time. Imagine the way a digital camera stitches together individual photos across a landscape to create a panoramic view wider than the actual lens.

“When you apply these technologies to a different group or a wider aperture of patients, you start to elucidate these time courses that are highly revelatory and can give us new targets, new biomarkers and new opportunities to intervene,” said Kahvejian, who has co-founded more than 10 companies since 2011, including Seres Therapeutics, Ring Therapeutics, Ampersand Biomedicines and more.

As with many Flagship companies, Etiome is built on the back of a platform rather than a therapeutic area. But one of the first conditions the biotech set its sights on was fatty liver disease, tracking the progression of people with healthy livers to ones that have accumulated fat, which leads to inflammation and then cirrhosis and “catastrophic consequences” if left untreated.

“We know this could be a silent disease, because most people don’t know their livers are steatotic or accumulating fat,” Kahvejian said. “But when you do retrospective analysis, you realize there’s a higher percentage of people than the ones who are detected.”

Through molecular signatures and biomarkers, Kahvejian and the Etiome team identified patients who weren’t aware they had fatty livers and pulled the data together to build a disease journey. Those biomarkers and new targets could lead to an interventional treatment, he said.

‘We’re not investors — we’re practitioners’

Flagship is known as a biotech VC firm, but Kahvejian doesn’t really see it that way.

“We’re not investors — we’re practitioners, we’re protagonists, we’re inventors, we’re scientists, which means we apply these things rather than seeking them out and investing in them,” Kahvejian said, pointing out that Etiome’s use of AI doesn’t arise from technological hype. “We’re not sorting the wheat from the chaff. Instead we’re taking an agnostic approach, and we use AI to digest data and elucidate interesting targets and causality. That mindset is part of our DNA, no pun intended.”

One of Flagship’s first forays into the world of AI was Cellarity, founded by Kahvejian in 2017 around a machine learning platform designed to unravel the mysteries of cellular behavior and generate medicines based on those findings. The company has a sickle cell disease treatment in the final steps of preclinical development.

“We’ve seen a hell of a lot of impact when we apply AI to the optimization and discovery of medicines,” Kahvejian said. “Whether that’s at Cellarity, whether it’s at Generate:Biomedicines, whether it’s at Alltrna, everywhere we’re using these genetic medicines or sequence-based medicines, we’ve started to turn them into programmable medicines thanks to AI.”

Rather than performing due diligence on what’s already happening in biotech, Flagship pushes forward into the next big thing, Kahvejian said.

“I’ve never done due diligence in my life — I do diligent science,” Kahvejian said. “The way we work is, instead of trying to identify companies or identify professors per se, we’re trying to imagine the next things we can create. We make mistakes, either with something that has no utility or isn’t as broad as we thought, but our process involves shooting for that all the time.”

That approach has helped Flagship keep its finger on the pulse of the biotech world, he said, while aiming beyond it.

“By definition, you’re going to create things that don’t exist — you’re going to reach into areas that are uncharted, off the beaten bath,” Kahvejian said. “And then you’re working your way backwards and asking, is this useful? Does it matter? Can we do it? If the answer is no, then fine, come back to Earth and take another leap.”

Platform devotion

Conceived just before the turn of the century, Flagship has been unwavering in its devotion to platform companies.

“For 25 years, we’ve felt that it’s a formula that works,” Kahvejian said. “We’ve always been steadfast about doing highly innovative and differentiated — for better or worse — technologies and companies.”

To illustrate that approach, Kahvejian uses the example of perhaps the most well-known Flagship company: Moderna, the biotech darling that altered the course of the COVID-19 pandemic. Vaccines weren’t always the trajectory for the fledgling mRNA specialist.

“Moderna wasn’t built by saying ‘mRNA is good for vaccines,’” he said. “It was built with the notion that a programmable molecule like mRNA could have a range of uses, making proteins inside the cell or on the surface of a cell, or be used for cancer, for rare disease, for infectious disease, and so on.”

That agnosticism toward disease targets could result in a lack of direction in some cases, Kahvejian acknowledged, but flexibility is the key. For Moderna, the flexibility allowed quick action in the face of a global health crisis.

“We let the science guide the thinking and resource our experimentation appropriately — not to spread it thin like peanut butter, but nor should we just take one rifle shot,” he said.

Innovation supply chain

Biotech investment has been difficult and selective over the past few years, but Flagship has weathered the storm more by degree than by direction.

“It’s more a tweak in how we execute rather than a wholesale change in strategy or change in philosophy,” Kahvejian said.

Flagship has stayed the course while others in the industry have shifted toward pragmatism, he said.

“There isn’t enough of that patient, visionary capital out there, and it’s unfortunate,” Kahvejian said.

But innovation in biotech is what feeds the whole life sciences armada, fueling tomorrow’s blockbusters and the pharma giants who depend on them. And so Kahvejian and others at Flagship, including CEO and founder Noubar Afeyan, believe in what they call the “innovation supply chain.”

“It’s something we envisioned a while back — [Afeyan] and I wrote an essay about the concept of an innovation supply chain in which the raw material for new medicines is new modalities, new understandings of biology, new targets,” Kahvejian said. “Pharma has been exceptionally good at making drugs against well-established targets, getting them through the clinic rigorously and deploying them into the market, but startups have zero business doing that on their own.”

Instead, Flagship collaborates with Big Pharma in a way that makes sense for both sides. Almost two years ago, Flagship and Pfizer teamed up to develop a pipeline of 10 drug candidates, equally sharing the investment load, with Flagship undertaking the early science and Pfizer gaining the opportunity to acquire the development program down the road. 

The two companies this month announced their sixth official collaboration within that agreement — a platform called Logica from Flagship’s Valo Health, designed for drug discovery in the autoimmune space.

“By building these innovation supply chain deals, we’re essentially taking the best of what each does and bringing it together in a way that’s derisked for both,” Kahvejian said. “We need new types of medicines to solve some of our really pressing challenges, and that can be valuable and derisked if we do it this way — we think it’s a beautiful solution.”

Recursion Pharmaceuticals teamed up with AI biotech Exscientia last year in a deal worth almost $700 million. The matchup marked the biggest life sciences AI merger to date and comes amid a “surge in AI partnerships and acquisitions in the past five years [that] signals the opportunities the technology offers to life sciences companies,” according to an EY report. 

Now, successfully integrating these companies into the more traditional drugmaking processes will take creative thinking and a full commitment to the changes ahead, said Recursion CFO and president of Recursion UK, Ben Taylor, who compared the current moment to “when biologics were growing and coming of age” about a decade ago.

“You had every different strategy come out of pharma, from going big to acquire assets, or going smaller to grow it from there, or not acquiring at all to grow entirely in house,” Taylor said. “You’re seeing the same thing happen, because most companies haven’t had a lot of validation coming through on AI platforms yet, and that’ll be a triggering part for a lot of pharma companies to start thinking about more.”

While Exscientia was struggling under the weight of losing a big Bayer partnership and leadership changes in the last few years, Recursion was building up its pipeline of AI-discovered drugs and securing high-profile partnerships. But the companies had a lot in common, and together they now have 10 programs in the pipeline and hundreds of millions of dollars in potential milestones lined up with Big Pharma partners like Sanofi and Roche, Taylor said.

While the new Recursion’s technology is promising and backed by world-leading computer chip maker NVIDIA, the company’s pipeline is early in clinical stages. Its most advanced program is in the rare disease cerebral cavernous malformation and has crossed the phase 2 threshold, while its programs in oncology and other rare diseases are earlier in the process or preclinical.

Although the supersized AI marriage is still a novel concept, Taylor believes life sciences leaders will start to see more of these deals down the road, bringing new technologies into the fold and changing the way drugs are made. 

Here, Taylor discusses the AI disruption in biopharma, what companies should be looking for as they explore partnerships and acquisitions, as well as the challenges associated with collecting and using big data to drive further shifts in the industry.

This interview has been edited for brevity and style.

PHARMAVOICE: AI is clearly becoming a disruptor in the life sciences — why is that happening at this point in time?

BEN TAYLOR: The right way to think about AI is as a far better tool than [what] has been available previously that gives you functionality that wasn’t there before. I would liken it to when tools like Excel were coming all of a sudden, and it was so much easier to do something on a spreadsheet rather than using a calculator. We’re seeing a similar level jump in computation, going from the traditional methods we’ve been using for the last 20 to 30 years to something where we can perform a highly efficient, multi-parameter function. 

In biology and chemistry, there are no simple problems — everything is a complex, multi-parameter problem. If, for instance, NVIDIA weren’t able to provide the world with their processors, you couldn’t even do the analysis, but beyond that, the architecture of the analysis is fundamentally changed to allow us to perform deep learning on big data problems. We’re finding a needle in a haystack, and there’s a huge variety of algorithms to optimize and, for example, figure out where this atom should be placed on this molecule. You’re not going to solve all of the different aspects to create a new molecule with a single algorithm — you need to integrate them.

Speaking of integration, why did the Recursion and Exscientia deal make sense from your perspective?

Both companies were founded about the same time 12 years ago with a very similar mission, which is drug discovery. At the time, there were new technologies but they weren’t predictive, and so even with some of the smartest people in the world, we still haven’t been able to solve the very basic, fundamental problem of a failure rate that’s over 95%. Why is that happening? It’s because we didn’t have predictive ways of understanding what’s going to happen later on in a clinical trial. You actually have to predict how a molecule should work, and both companies had that vision with Recursion focusing on the biology and Excientia focusing on the chemistry. It’s been fascinating to watch the integration because it’s been much easier than you would imagine. Now the chemists have all these amazing tools to play with in biology, and the biologists have these amazing tools to play with in chemistry, and it came together so powerfully.

What should drugmakers be looking for as they scan the horizon for AI partners or acquisitions?

Look for the use case. Look for the validation. We’re in an industry where lots of people have lots of ideas on how to do both, and the ideas all sound like they’re going toward the same goal, or they use the same language behind it. So the only way you can really differentiate is by defining the actual product you produce with a platform or technology. It’s funny, because I’ve been deep in this for years, and I read a press release, and I’m like, ‘Oh, it sounds like they do exactly what we do. And then I dig in, and it’s like, ‘Oh, they do nothing like what we do.’ The only way you can really get your arms around that is to see what’s being produced.

On the flipside, what should AI companies be looking at when they team up with the life sciences?

The No. 1 thing is commitment, because it’s always easy to find a partner or even some money around new and exciting technologies. In fact, I think a lot of partners have a part of their budget that’s set aside to say, ‘Hey, go and spend this on new, crazy ideas.’ The partners that make a difference — if you look at our Roche and Sanofi partnerships, for example, with multiple programs each — their side comes in as committed as we are, and that comes from the top of the organization all the way through. When they’ve had relationships that haven’t progressed as quickly, it’s almost always because you just don’t have that commitment and that sense of doing whatever it takes to get this done. You’ve got to invest the time. If you’re not bought in on the process changing, it’s like taking an analog cable and putting it in the middle of a fiber optic — either way, you’re limited by whichever is the lowest quality point. So if a large pharma is saying they’re going to run everything traditionally, then insert AI for three months and go back to the traditional method. You’re going to lose most of the benefits of the more novel platform that way. In our relationship with Sanofi and Roche, and all of our relationships, we’re able to break down a lot of silos into an AI-first environment.

If you think about the pharma decision making process, it takes a lot to move through it. There are a lot of different signoffs, committees, groups — all sorts of things. I can always tell the companies that are committed to AI because you see that smoothness of decision making, and that engagement comes from all levels inside the organization. If you don’t have that, getting the milestone across can be really tough, because it always requires another level of approval.

If we look at data as currency in the life sciences, is there a limit to the availability for AI systems to make use of?

I don’t think there’s a limit. But most of the data isn’t worth anything. A lot of the data that has been collected historically generally isn’t in the right format or is very specific and not that useful. We use public datasets, or when we’re in a partnership we use their data and dig into everything we can, but honestly it only gets us to a very rough starting position. More useful is creating a fit-for-purpose dataset to be able to answer questions — and it’s fit directly for the purpose of the questions you’re asking. Since we work in proprietary drugs, and if you actually want to come up with novel concepts, you have to start with something that’s going to answer very specific questions.

So you're going to have to create your own data on that as well, right? That's the No. 1 concern. We once had a discussion with a pharma partner who was asking us to come in and take a look at their old data and help them build models with it. The conclusion we came to was that it was going to be far more efficient and probably predictive to actually recreate all of the data that they had been doing in a much smaller context. You could get to it without trying to recreate 30 years of data, right? Because all of their data was in different formats and based on very specific projects, you would have had an army of people working night and day to try and wrangle that into shape to where you could actually use it. So that's something I think a lot of people miss.