Making the Medicines
Most Likely to Work

Approximately 250 million target-disease pairs collectively represent the "cures solution space" to treat all known diseases. Selecting which target-disease opportunities to prioritize is the hard part.

Fortunately, as human clinical and genetics datasets continue to grow rapidly each year, the strongest potential disease targets are continually revealed. And as large language and machine learning models become increasingly capable, it becomes possible to apply data science to systematically evaluate all lines of evidence in tandem to rank and make quantitative predictions about the very best new drug creation opportunities out of millions.

Over the last decade, 10+ new therapeutic modalities have been invented, refined, and clinically validated, significantly broadening the drug-ability of historically challenging targets and unlocking superior new drug safety and efficacy properties.

In parallel, revolutions in drug discovery technologies--AI-enabled structural prediction, in silico screening and design, high-throughput robotics, and advanced new compound libraries and screening methods--further increase success rates, speed, and efficiency.

When combined, these extraordinary advances open up a golden era for drug discovery in which new, better drugs can be created for 5,000+ untreated or underserved disease populations.

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 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.

Over the last decade, as large Pharma companies shuttered their early-stage research centers, 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.