GenAI’s Real Potential in Biopharma: As a Cross-Function R&D Assistant

February 9, 2024

Commentary Contributed by Daniel Jamieson, Biorelate

February 9, 2024

In biopharma R&D there is growing momentum around the use of Generative AI, the deep-learning algorithms capable of distilling complex knowledge into easily digestible summaries. But so far companies have been relatively modest in their ambitions for the technology. Although applying GenAI discretely within individual departments makes sense for many use cases, the greater opportunity is to harness it across all functions—as a super decision intelligence tool, bringing all aspects of the R&D strategy together. 

The Unique Role GenAI Can Play 

A subset of artificial intelligence, GenAI can very rapidly distil key information and insights—via machine learning—from vast knowledge banks and create new output from it which is intuitive and easy to digest. In drug discovery, it can be used to harness smart, mass-scale data analytics in a reliable and accessible way.  

It can cost anywhere between $2.5 billion and more than $6 billion to bring a novel drug to market today given the high risk of failure, especially as the complexity of therapies grows. The challenge is to determine those opportunities worth pursuing, based on everything important that is already known about that field.  

The problem is how to get to those insights reliably and efficiently. Published research and other texts may contain rich knowledge, but largely all of this content remains un-curated and daunting in scale. Cause-and-effect-like relationships may be abundant across the available sources, for instance, but are generally difficult to pinpoint, connect, and analyze. Targeted use of AI makes it possible to capture and link findings so discovery teams can infer new meaning and insights.  

In one scientific study, for instance, researchers might have examined how a specific drug triggers the activation of a particular protein. Concurrently, a separate study has highlighted that this activated protein is linked to the onset of hypertension. It is only once these discoveries are connected that a potential hypothesis emerges: in this case, that the drug in question might pose a risk for inducing hypertension.  

The powerful enabler, thanks to GenAI, is the ability to structure and analyze valuable data that up to now has been locked in text. That could be from internal archives and/or from millions of scientific research articles, in combination with all the other structured data sources including transcriptomics and proteomics.  

Counting the Gains 

Every drug discovery company needs to optimize spending and control costs along the development cycle. Anything that can help organizations fail faster and improve the speed to approval and market access, by reducing the need for additional experiments, helps toward that goal. At a macro scale across potentially hundreds of drug programs, even just a 1-2% improvement in a drug’s chances of success, accelerated market delivery, or the chance to capitalize on additional opportunities, can have a huge impact across an entire drug discovery pipeline.  

Targeted, appropriate application of AI to elicit valuable insights from across vast research archives can yield 10- to 100-fold improvements at certain parts of the clinical trials process. Expediting early target selection to Phase I clinical trials from what might have been four and a half years to just one represents a significant improvement in speed.  

AI Innovation Within a Broader Strategy 

Up to now it has been rare to see a single business-level objective that transcends R&D and extends through to commercial planning in a way that can influence everything in an integrated way. This is a missed opportunity; fragmented operations and budgets can prevent drug developers from capitalizing on the broader benefits of AI-enabled insights. 

The pinnacle would be for GenAI to serve as a kind of “chatbot” or knowledge assistant for the drug development organization. Once data can be linked right across the enterprise, even if each function or therapy area is running its own strategy and programs, it becomes possible to access and apply guidance based on the full picture of what's going on.  

GenAI provides a powerful new way to interface with data and other AI models that contribute to the bigger picture—allowing users to ask not only about the financial performance of biopharma companies and their drugs, but more probingly: “What are the best-performing drugs on the market, and what are their common mechanisms of action?”  

The technology continues to evolve of course, too, adding to the possibilities. In 2024, multi-modal algorithms (MLMs) will become a large and growing trend, presenting the opportunity for teams to interrogate not only text, but also images, sound, and video.  

More important than the potential applications however, is the robustness and reliability of the AI capability. For the technology to be trusted in a Life Sciences R&D context, knowledge sources need be both transparent and validated, along with how connections have been made. This is being addressed via approaches to data curation and quality control such as Retrieval-Augmented Generation (RAG).  

GenAI’s potential to transform the efficiency and effectiveness of drug discovery is considerable, as long as biopharma companies are discerning in their choice and application of tools. 


Daniel Jamieson is CEO of Biorelate, which enables curation and smart linking of currently digitally unsearchable materials, and external archives of biomedical knowledge, to accelerate new drug discovery. Via its platform, Galactic AI, Biorelate uses AI-led curation to provide and enable the insights that matter to scientists and organisations developing the innovations of the future, helping them to advance and expedite the delivery of biomedical breakthroughs. He can be reached at