The AI foundational model has arrived and it will be a game changer for pharmaceutical R&D. But this is no Chat-GPT plug-in. Effectively leveraging AI for patient and business outcomes requires a fundamental cultural and organizational shift, says Deep Genomics Founder, Brendan Frey.
The past two years have ushered in an era of immense promise for artificial intelligence (AI) in the pharmaceutical industry. Unlike the previous generation of AI that emerged on the pharma scene between 2013 and 2023 and did not live up to promises, there are good reasons why this new generation of AI, called foundation models, ought to accelerate drug discovery and uncover novel targets and therapeutics that would unlikely be found otherwise.
Recently, amidst the fanfare and excitement, several AI-driven drug candidates – in areas ranging from cancer to dermatology – have quietly met their demise, failing to make it into commercial production. For some, this reality has cast a shadow of skepticism over the true capabilities of AI in the pharmaceutical industry. Yet, what the headlines fail to capture is a significant shift occurring behind the scenes – a fundamental change in how AI is being deployed and harnessed.
The pharmaceutical industry is moving away from an approach where AI is task-driven, narrowly focused on a single, isolated problem. Instead, companies are embracing a new paradigm: AI foundation models. These models, trained on very large and broad datasets – we’re talking about billions or even trillions of datapoints – are versatile, and capable of tackling a range of complex challenges simultaneously. They exhibit properties of emergent intelligence, wherein the foundation model can solve problems that it was not designed to solve, often without being given any additional information or data. Think of an entirely new kind of ChatGPT, one that is not trained to understand human language, but is instead trained to understand human genetics, molecular biology and cell biology.
Here are three things pharmaceutical companies should know in order to leverage AI foundation models for positive and impactful change.
Technological shift
As the pharmaceutical industry grapples with AI promises and pitfalls, understanding the power and potential of AI foundation models is crucial. The first lesson? This is no Chat-GPT plug-in. It cannot be overstated how truly revolutionary the shift will be from the way we used to utilize AI to the way we see foundation models being used, just ahead on the horizon.
Imagine searching for gold, but you’re only allowed to dig at one location, in one direction and 5 feet deep. This is how the industry started with AI, focusing AI models on narrow questions. Now imagine being able to search an entire region for gold, at any location, in any direction and at any depth. This is what moving to an AI foundation model feels like.
At Deep Genomics, our focus is on genome biology. Shifting to an AI foundation model meant moving from 40 separate, specialized AI models that answered narrow questions, to one comprehensive model that understands a broad swath of genome biology, which includes molecular and cellular biology. This foundation model realizes synergies between the narrower tasks and as a result becomes much more powerful at all of them. Also, whereas it was not possible to scale up the 40 separate models to tackle bigger datasets and task-specific datasets, such as patient data, scaling up the foundation model is relatively straightforward.
Another example of foundation models is those that have been deployed for predicting protein folding, where the industry has unleashed a ton of innovation.
Genome biology is very complex and compared to other areas, succeeding will require more compute, more data, and more experienced people. This introduces an important lesson: your people don’t need to speak the same language, but they do need to understand each other.
Multilingualism is the key to success
Effectively leveraging AI foundation models to reap the benefits requires more than just implementing the technology itself. Truly disruptive technologies demand a foundational shift in an organization’s culture, weaving cross-disciplinary collaboration into the fabric of the business.
In the past, both pharmaceutical companies attempting to leverage AI and AI companies attempting to apply their models to drug design have faced significant challenges. These setbacks are rooted in a failure to bridge the cultural and language barriers between the disparate disciplines involved. Adopting AI foundation models requires a fundamental mindset shift that fosters seamless collaboration across domains. At the core of this transformation is the need for “multilingualism” – the ability to facilitate effective dialogue between experimental biologists and AI researchers.
Multilingualism has to be a core value, recognizing that true breakthroughs in AI-driven drug discovery can only be achieved when researchers transcend the silos of their respective fields. This new generation of “amphibious” scientists, able to operate fluidly in both the wet labs where tissue samples are tested and the dry labs where AI researchers work, is pivotal for building a successful company in this area.
For example, whereas the tech sector has a rich history of collaboration and open sourcing, this ethos is relatively new to the pharmaceutical industry. By open sourcing GenomeKit, a set of tools for accessing and manipulating genomes, we are participating in a broader cultural shift towards openness that we believe can raise the tide for the industry. Companies adopting disruptive AI technologies like foundation models must be willing to open up, collaborate across disciplines, and crowdsource solutions – ultimately bringing vital new medicines to patients faster.
A new kind of partnership
Merely bolting on AI capabilities to existing pharmaceutical organizations is insufficient. Generating the wide and general outputs that AI foundation models promise requires a level of deep integration and true collaboration that cannot be achieved through traditional approaches.
The future success of AI in the pharmaceutical industry hinges on forging a new class of strategic partnerships between traditional pharmaceutical companies and an emerging class of “techbios”. These techbios are built from the ground up with AI and information technology at their core, both technologically and culturally, enabling integration and collaboration between biologists, chemists, and data scientists, both internally and externally.
This approach necessitates a unique kind of partnership – one that brings together the domain expertise of pharmaceutical companies with the cutting-edge AI capabilities and culture of techbio organizations.
People hear so much about ‘artificial intelligence’ these days that they have become accustomed to it as a kind of white noise backdrop to every job, sector, and industry. It is, however, worth taking the time to understand how the deployment of foundation models in particular represents a different paradigm shift that will impact pharmaceutical R&D more profoundly than all prior AI advancements that have come before it.
Brendan Frey
Brendan Frey is Founder and CIO of Deep Genomics.
