Niven R. Narain, Ph.D., President & CEO, BPGbio.
In late 2023, trillion-dollar chipmaker and high-end graphics processing unit (GPU) manufacturer Nvidia joined forces with Recursion, one of the few AI biotech companies that have assets in Phase 2 or Phase 3 trials, to accelerate foundational models in biology and chemistry at an unprecedented scale in the biopharma space.
This collaboration, along with others, exemplifies the growing “techbio” industry, which leverages technology to expedite drug discovery. As Kimberly Powell, head of Nvidia’s healthcare segment, aptly shared in a Bloomberg report, data can “drive what biology they’re going after, instead of the biology influencing what technology they need to use,” emphasizing the synergy between the two fields.
However, it’s important to acknowledge the long history of collaboration between AI and biotech. Since the birth of bioinformatics in the 1960s, scientists have been harnessing computational approaches like analyzing protein sequences to make advancements in drug discovery. The present iteration of techbio builds upon this rich foundation, taking the collaboration to an unprecedented scale with the power of modern computing.
Today’s powerful new technologies can unlock valuable insights from existing data, dramatically accelerating drug discovery. For example, Insilico Medicine has demonstrated this potential, leveraging AI to discover and synthesize 80 molecules for clinical trials within 18 months—a process that traditionally took years. This speed of preclinical discovery gives hope of a much shorter time for a drug to make it to market, but will that hope translate into reality?
AI-Driven Drug Discovery: Cheaper And Faster?
Some predict that AI-driven drug development companies (driven by technologists and supported by biologists using existing public data) that use generative AI and machine learning to discover new targets and new molecules can make drug discovery both faster and cheaper.
However, this acceleration has yet to warrant a successful Phase 3 clinical trial. Additionally, these innovative methods are proving to not be as cost-effective as originally purported, as seen with Recursion’s 2023 R&D expenses surging to $241.2 million in FY 2023 ($85.5 million more than 2022). They’ve also experienced clinical setbacks, as seen with Exscientia closing a study of its cancer drug candidate EXS-21546.
These statistics do raise concerns about the efficacy of an AI-led approach, prompting questions about the data’s sufficiency for successful drug development. Fundamentally, are we oversimplifying the intricate nature of biology? Or, is it not revolutionary but simply incremental improvements in efficiency?
AI In Drug Discovery: A Powerful Ally
While the full potential of AI in drug discovery is still unfolding, its early impact is undeniable. Pharmaceutical companies that prioritize a biology-first approach, where biologists still study biological samples and guide the use of AI and data science, are reaping the benefits of accelerated drug development.
AI significantly reduced the development time of Moderna’s mRNA Covid-19 vaccine and has played a crucial role in the development of Nimbus Therapeutics’ TYK2 inhibitor for autoimmune diseases. It also assisted in our pancreatic cancer and glioblastoma trials by efficiently identifying patients, which was a critical step.
Biologists’ expertise in disease pathways steers AI analysis of massive datasets, identifying promising drug candidate patterns. However, their knowledge remains vital to validate these targets and ensure they address the root cause. AI acts as a powerful tool, empowering biologists to cut through complexity, prioritize targets and accelerate life-saving treatments.
It’s crucial to see AI as an enabler, not a replacement. Drug discovery demands the combined expertise of data scientists, MDs, Ph.D.s and practitioners, and collaboration is key.
The Human-Tech Alliance: Powering The Future Of Drug Discovery
The future of drug discovery isn’t a solitary endeavor. Rather, it thrives on a powerful alliance—the integration of biology and technology. While the full potential of this union remains to be seen, we know this collaboration holds the key to groundbreaking discoveries.
To crack this intricate code, we need a collaborative ecosystem that fosters fresh ideas, diverse perspectives and unconventional thinking. As former McKinsey partner Lydia The aptly stated: “Technology doesn’t supplant the people. Rather, it will enable scientists to do things faster and better—and potentially develop insights that humans would not be able to develop at all.”
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
