Data Science-Driven
Drug Discovery

Approximately 250 million target-disease pairs collectively represent the "cures solution space" to treat and prevent all diseases. Selecting which target-disease pairs are worthy of investing significant research effort and capital is the hard part.

Fortunately, as human genetics evidence and other biomedical data sets continue to grow exponentially, strong new human evidence for hundreds of novel disease targets emerges each year, enabling data-driven approaches for prioritization.

And as Large Language Models and other forms of AI become increasingly capable, it becomes possible for the first time ever to  systematically evaluate billions of interconnected research datapoints to help identify the very best new opportunities.

Over the last decade, as large Pharma companies shuttered their early research centers (in favor of acquiring ~70% of their new drug pipelines from external biotech), a flood of talented R&D scientists made their way into global contract research organizations (CROs) dramatically expanding their offerings, capabilities, and scale.  

As a result, a flourishing new virtual R&D economy now offers democratized, on-demand access to a wide range of highly specialized R&D capabilities, enabling biotech companies to 1) take better advantage of a broader swathe of new technologies to increase success rates, drug quality, pace, and efficiency; and 2) convert fixed overhead into fully variable expenses under lean, specialized management teams, eliminating the 40-60% of capex historically focused on building out labs and hiring generalists.




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Science is inherently global, and yet most US biotech remains local, with ~60-70% of US biotech VC invested in just 2 states.  

Fortunately, the proliferation of remote collaboration tools unlocks extraordinary opportunities to reinvent traditional scientific staffing models to overcome geographic constraints and engage with the very best scientific talent wherever it may be found.

Large, fully virtual teams can now work together seamlessly across countries, research disciplines, vendors, and both academia and industry, collaboratively crafting world-class R&D strategies to improve quality and remove scientific blind spots.

Biotechs have historically focused on shepherding a small number of programs from discovery through to advanced stages of clinical development. However, as top biopharma companies increasingly prioritize external acquisitions and early partnerships to fill 60%+ of their new drug pipelines, novel portfolio designs and business strategies emerge to enhance risk-adjusted returns.

Optimizing for earlier program exits can dramatically reduce exposure to the stages of the highest clinical failure risk. Designing larger, risk-diversified portfolios can mitigate binary outcomes while enhancing economies of scale and cross-program R&D synergies. And novel "asset-centric" corporate structures create the opportunity to tax efficiently sell individual programs without having to sell operations, unlocking new incentive structures and lean, evergreen value creation strategies.