This episode reveals how Metanova is merging crypto, AI, and pharma to build a decentralized drug discovery platform, aiming to solve the high-cost, slow-moving crisis in medical advancement.
The Origin of Metanova
- Micaela Bazo and Pedro Penna connected through the Decentralized Science (DeSci) community, a movement focused on using web3 tools to create more open and collaborative scientific research. They shared a vision for a new approach to drug development that leverages Pedro's expertise in AI drug design and Micaela's focus on virtual biotech and geographical arbitrage.
- Coming from Peru and Brazil, respectively, they recognized an opportunity to address the structural failures of traditional drug development.
- Their goal is to accelerate timelines and reduce costs by optimizing the process through a decentralized, global model.
- Micaela highlights their shared alignment: "We instantly recognized that we had, you know, shared interests for a new kind of approach to drug development that leveraged his experience with AI, drug design, and also that took the opportunity of capitalizing on virtual biotech and truly decentralized science."
A Focus on Behavioral Drugs
- Metanova targets what they term "behavioral drugs," moving beyond a purely disease-centric model. This approach focuses on lifestyle factors that are upstream determinants of health, such as caloric restriction, sleep, and focus.
- By targeting the root behaviors, they aim to develop drugs that can prevent or treat a wide range of conditions, from heart disease to neurodegenerative disorders.
- This "upstream" strategy creates a strong economic case, as a single drug could address multiple large markets simultaneously.
- Micaela explains this creates a point of intersection for many diseases, allowing them to treat more people and build a stronger commercial proposal.
The Subnet Architecture: A Decentralized Search Problem
- Metanova's subnet on the Bittensor network operates as a decentralized drug screening platform. The core task for miners is not just raw simulation but an intelligent search for promising molecules.
- Metanova provides miners with two key components:
- Well-benchmarked AI models that predict a molecule's binding affinity to a specific protein target.
- Curated chemical universes of synthesizable molecules, ensuring a high likelihood that discoveries can be physically created.
- Miners are incentivized to develop clever algorithmic search strategies to navigate these billion-molecule universes and identify high-potential regions for simulation.
- Pedro clarifies the incentive: "What is incentivized is clever algorithmic searches. So they can go through the chemical universes... and prioritize the molecules that should be going through this specific evaluation."
Actionable Discoveries: The Importance of Synthesizable Molecules
- A key principle for Metanova is ensuring that all miner submissions are actionable. The molecules they search for are theoretical but designed to be synthesizable, meaning they are based on available chemical precursors and known reaction rules.
- This approach avoids the common pitfall of discovering theoretically potent molecules that cannot be created in a lab.
- Micaela emphasizes this practical focus: "One thing that we are trying to do that sets us apart is make sure that the submissions that we receive from the miners are actionable."
- Their combinatorial dataset allows miners to use generative AI approaches that mimic real-world synthesis, expanding the search space while maintaining practicality.
Bridging Crypto and Pharma with Contract Research Organizations (CROs)
- To validate their digital discoveries, Metanova partners with Contract Research Organizations (CROs), which are third-party labs that provide outsourced research services to the pharmaceutical and biotech industries.
- This strategy allows Metanova to remain a lean, virtual biotech without the overhead of its own wet labs.
- Using the same CROs as major pharmaceutical companies provides an "oracle" of ground truth, ensuring their data is seen as valid and credible by traditional players.
- Pedro notes the strategic value: "Your data is going to be valid because you're going to be using the same lab that very big companies are also using as service providers."
Monetization and the "Platform Valuation Premium"
- Metanova has a multi-faceted monetization strategy that provides significant optionality. While the ultimate goal is developing proprietary drug assets, the platform itself is a valuable, revenue-generating machine.
- Potential Revenue Streams:
- Drug screening as a service for other companies.
- AI model training as a service.
- Selling validated data from CRO testing.
- The "holy grail" remains developing a drug asset, which can lead to deals worth hundreds of millions or even billions of dollars.
- Micaela points to a key investor trend: the return of the "platform valuation premium," where a company's valuation is boosted by the potential of its underlying technology to generate multiple assets, not just one.
Navigating Regulation and the "Dog-to-Human" Pipeline
- While viewing the FDA as the ultimate regulatory goal, Metanova employs a pragmatic strategy to navigate the lengthy and expensive approval process. This includes leveraging global partnerships and a novel go-to-market pipeline.
- They draw inspiration from companies like Loyal Biotech, which follows a "dog-to-human" pipeline.
- This involves first seeking approval for pets, which is faster and cheaper, and then using the sales revenue and animal model data to fund and de-risk the subsequent human trials.
- This approach is particularly effective for behavioral and longevity drugs, where human trials can be exceptionally long.
Target Selection: A Mix of Science, Art, and Market Dynamics
- Choosing the right biological target is a critical and complex decision. The process involves a blend of deep scientific validation, market analysis, and strategic foresight.
- Key Scientific Factors:
- Genetic Validation: Is there evidence linking mutations in the target to a specific condition?
- Biomarkers: Are there measurable indicators that can track the drug's effect?
- Translational Models: Are there reliable animal models to test the drug's safety and efficacy?
- Business Factors:
- Market interest from academia and big pharma.
- The potential for insurance coverage or out-of-pocket payment.
- Pedro explains the complexity: "Many companies they don't even talk about their targets. They keep it confidential because of how much of a mix between science and art this is."
Pragmatism Over Hype: Small Molecules vs. Gene Editing
- Micaela and Pedro position Metanova's focus on small molecules as a pragmatic choice designed for maximum real-world impact. While technologies like gene editing and brain-computer interfaces are promising, they are decades away from widespread accessibility.
- Small molecule drugs leverage existing, perfected global manufacturing and distribution systems, making them scalable to a global population.
- Pedro adds a critical safety insight: small molecule drugs are reversible. If a patient experiences side effects, they can simply stop taking the drug, an option not available with permanent genetic edits.
- Micaela frames their approach as innovating on the existing system: "How can we make the best of the existing system that we have in front of us and innovate on top of it?"
Miner Performance and the Power of the Swarm
- The initial results from miners on the Metanova subnet have been highly promising, validating the power of a decentralized, competitive network.
- Miners have proven exceptionally skilled at finding the "shortest path" to rewards, which includes identifying and exploiting any weaknesses in the provided AI models, forcing rapid improvement.
- Submissions have shown high chemical diversity while also including molecules structurally similar to known effective compounds, indicating the search is both novel and grounded.
- Micaela sees this as proof of the Bittensor model: "You can find alignment amongst all these different kinds of actors... we don't need to agree but we can work towards a common goal."
Advice for Future Bittensor Builders
- Micaela and Pedro offer advice for others looking to apply the Bittensor model to new industries, emphasizing boldness and a deep understanding of the problem space.
- Micaela's Advice: Be bold and tackle problems that truly benefit from a decentralized strategy. Use the network's competitive nature to pressure-test your models and achieve a balance of fierce competition and cooperation.
- Pedro's Advice: "Be sure that you're bringing them the most exciting and hard problems to go for because they're going to ask for it for sure." Miners are exceptional and thrive on complex challenges.
Conclusion
Metanova’s strategy offers a blueprint for applying Crypto AI to disrupt legacy industries. By combining decentralized R&D for capital efficiency with traditional validation methods for credibility, they are building a pragmatic bridge to real-world impact. Investors should watch this hybrid model closely as a potential standard for DeSci adoption.
