Key Takeaways
- The highest leverage for AI in healthcare isn't identifying new disease targets or optimizing clinical trials; it's in the drug discovery and design process itself.
- Genesis Molecular AI, co-founded by Evan Fineberg, focuses on tackling "undruggable" protein targets — diseases where the biology is understood but no effective, selective therapy exists.
- There's immense value for patients and the market in developing "best-in-class" treatments, even for conditions with existing drugs. Think of later-generation ALK inhibitors that offer significant improvements.
- The core challenge often isn't understanding the disease mechanism, but designing molecules that are selective, potent, and safe enough to overcome the inherent difficulties of specific protein targets.
AI's Untapped Frontier: Undruggable Targets
Many ambitious founders chase novel opportunities, aiming to be first-in-class. But Evan Fineberg, co-founder of Genesis Molecular AI, points to a different, less obvious but perhaps more impactful path for artificial intelligence in medicine. His core contention is that the most impactful application of AI isn't in finding new disease targets or streamlining clinical trials, but in the gritty, complex work of drug design.
Fineberg explains that for many diseases, the underlying biology is well understood. The problem isn't a lack of knowledge about what's causing the condition, but a lack of a selective and safe treatment. He puts it plainly: “We know it's causing your condition, but we do not have a selective therapy. There is no precision medicine for your condition.” Genesis Molecular AI aims to create solutions for these "undruggable" targets, transforming moments of patient despair into moments of hope with specific, effective treatments.
This means focusing AI where the human intuition and traditional methods often falter: designing molecules that can precisely hit a difficult protein target without causing off-target effects. It's about bridging the gap between biological understanding and therapeutic reality.
Beyond First-In-Class: The Quest for Superiority
The allure of being "first-in-class" can often overshadow another crucial area of value: "best-in-class" therapies. Fineberg makes a compelling argument for the latter, stating, "I think there's not only value in first in class, there's enormous value for patients in best-in-class too." He points to examples like later-generation ALK inhibitors, where subsequent drugs significantly improve upon earlier ones, offering better outcomes for patients.
It's easy to assume that "easier" targets have all been addressed, leaving only the truly complex ones for new tech. Fineberg challenges this "false dichotomy of easy versus hard targets." Even in areas where drugs exist, there's often vast room for improvement in selectivity, potency, and safety. Genesis Molecular AI deliberately seeks to innovate in these areas, backed by a team of "accomplished drug hunters" who understand the nuances of bringing a superior molecule to market. The ultimate goal, as Fineberg reminds us, is straightforward: "It only matters if we end up helping patients and their families at the end of the day."
What to Do With This
If you're building in a mature industry or tackling a complex problem, resist the urge to only seek entirely novel markets. Instead, identify existing "good enough" solutions that still present significant pain points around efficacy, safety, or selectivity. Then, apply your technical advantage—whether it's AI, a novel material, or a unique process—to build a genuinely best-in-class product that solves those specific bottlenecks for a defined user base.