Data Science-Driven
Drug Discovery

Related Sciences (RS) uses a proprietary AI/ML platform to identify the best new drug discovery opportunities for every disease, assembles decentralized global teams of top researchers to guide the science, and then invests in and leads the efficient discovery of valuable new medicines via preclinical-specialized R&D platforms

Building a Specialized New Investment Model

Innovating on biotech's operating, R&D, and business models itself can be a critical force-multiplier and accelerator in the quest to bring the next great generation of medicines to patients. As such, we study big questions like:

What Factors Increase the Chances of Success?

RS spent years quantitatively analyzing all of global biopharma's historical successes and failures in order to elucidate the specific factors that most impact a drug's odds of clinical success, and a company's chances of economic success. What if a new systematic set of principles and strategies based on these critical factors could significantly improve success rates? 1, 2, 5

What Innovations Enhance Risk-Reward?

Today ~99.7% of the world’s institutional investors have zero exposure to biotech, with only ~250 firms making 2+ therapeutics investments per year 3. What if new tech-enabled operating models and more efficient R&D strategies could improve the sector's accessibility and economic appeal,  expanding capital availability for the drugs and companies that truly "deserve to be made"?

Are the Best Discoveries Made into Medicines?

Analysis of all current biomedical knowledge on the RS data platform reveals the surprising finding that ~75% of all target-disease opportunities with strong cumulative evidence of links have never been tried: 23,000+ opportunities spanning 3,800+ individual targets for 4,200+ diseases are just waiting to be made into promising new medicines. 4

Many Parallel Revolutions

The last decade's transformational innovations in drug discovery technologies, global contract R&D services, AI, biomedical data, therapeutic modalities, screening libraries, and remote connectivity collectively create an extraordinary new menu of advantages to discover valuable new medicines more efficiently and with greater odds of success than ever before.

Biomedical Data and AI

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.

Virtual R&D Economy

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.

Remote Scientific Collaboration

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.

Operating Model and Strategy

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.

A Fully-Integrated
Drug Discovery Ecosystem

RS combines 15+ modular innovations into a fully-integrated drug discovery platform designed to lower costs, maximize R&D technology advantages, and systematically mitigate key historical sources of risk.

AI-Powered Opportunity Ranking

Preclinical-Specialized Strategy

Efficient Operating Architecture

Decentralized R&D Platforms

Finding the Best Opportunities with Data Science

RS Facets TM️ AI/ML Platform

Must 90%+ of drugs inevitably fail? Or can programs with far higher chances of success be identified prospectively?  RS FacetsTM is a proprietary machine learning platform designed to ingest all activities in global biomedicine and systematically predict the best new drug discovery opportunities for every disease on an unbiased, quantified, fully explainable basis.

Innovation Stack

3 Proprietary Layers Unlock Extraordinary Insights

See Everything

All of Biomedicine in One Dataset

Assess Risk

Quantitative Opportunity Ranking

Choose Wisely

Predictive AI/ML Models

Enabling World-Class Drug Discovery

Preclinical Specialization

On a risk-adjusted basis, preclinical drug discovery generates the highest risk-adjusted expected returns of any stage in drug development.2 RS specializes in this attractive stage of R&D, building specialized teams, methods, portfolio designs, and strategies to increase the odds of successful early program exits to the 600+ top biopharma acquirers.

Preclinical-Specialized Staff

Data-Designed Preclinical Portfolios

Early Partnering and Exit Strategy

Better Science, Greater Efficiency

Efficient New Operating Model

RS combines a novel decentralized team science model, portfolio-based R&D, a fully virtual operating model, and a tax-efficient and asset-centric corporate structure, to reduce its cost structures by 40%+, minimize required overhead and capital expenditures, and enhance their scientific quality, depth, breadth, and flexibility.

Decentralized Team Science

Synergies and Economies of Scale

Fully Virtual Architecture

Maximizing Technology Advantages

Decentralized R&D Platforms

RS continually curates and partners with the world's most advanced drug discovery service providers to provide centralized, preferred access to advanced R&D capabilities, broad therapeutic modalities, and specialized technical experts.

Curated Global R&D Partnerships

Discovery Technologies

Therapeutic Technologies

Indication Strategy


RS is led by a multi-disciplinary team of drug discovery, data science, and venture capital veterans who have founded, built, and led biotech companies worth billions, and is backed by a luminary group of investors.


RS builds, launches, and manages one new company portfolio every 1-2 years, each of which is comprised of a “constellation” of the top 0.001% of all drug opportunities in an ascendant area of research and commercial interest.


Improving drug discovery efficiency and success rates to benefit all of biomedicine's important stakeholders:


A greater number of transformational new medicines for virtually all types of disease are discovered sooner and successfully brought to market more often.


Breakthrough research is successfully translated into medicines more often, and our new model enables leading researchers to contribute to biotech R&D without needing to leave academia.


Biotech company failure is significantly reduced, driving significantly lower job turnover and training a deeper bench of capable biotech operators for the next generation of drugs.


Supply of high quality, clinic-ready drugs is dramatically increased and made available at acquisition prices that both incentivize biotech and are sustainable for pharma’s own economic models.


Better biotech risk-reward drives a much greater proportion of investor wins broadening interest among institutional investors to fund future biotech innovations.