Crypto Meets Chemistry :: Bittensor SN68 NOVA, Decentralized AI Drug Discovery

This episode delves into how Bittensor's Subnet 68 (Nova) merges decentralized AI, crypto incentives, and pharmaceutical research to accelerate drug discovery, potentially disrupting a trillion-dollar industry plagued by inefficiency.

Introduction to Nova: Bridging Crypto, AI, and Pharma

Miko, Pedro, and Amanda from Metanova Labs introduce Nova (Bittensor Subnet 68), a project operating at the intersection of AI, cryptocurrency, and pharmaceuticals—three sectors known for immense value creation. They argue that merging these fields can overcome their individual limitations. The core mission extends beyond financial metrics, aiming to accelerate medical innovation with potentially life-changing impacts, creating a feedback loop between digital advancements and real-world value.

The Crisis in Traditional Drug Development

The discussion highlights the historical impact of drug development, citing aspirin (1899) and the subsequent 42-year increase in life expectancy. Despite a trillion-dollar global market, the industry faces a crisis: developing a single drug averages $2.6 billion and 10 years, with only a ~10% success rate. This high-risk environment often leads companies to focus on minor modifications of existing drugs ("patent busting") rather than true innovation.

AI's Promise and Pitfalls in Drug Discovery

AI has been explored for drug discovery since the 1960s, but only recent advances in data processing have made it impactful. AI promises to make drug discovery better, faster, cheaper, and more novel by shortlisting promising molecules early and exploring uncharted regions of the "chemical universe." However, Amanda notes the reality has been disappointing, largely because AI development often occurs in isolated research institutions without access to diverse, real-world data needed for robust predictive power. Current incentives often misalign with the goal of finding genuinely safe and effective drugs.

Bittensor as a Solution: Incentives, Talent, and Funding

The team posits that Bittensor can uniquely address the "valley of death" in early-stage drug development. Nova leverages Bittensor's token emissions for early-stage funding, enabling bolder, "moonshot" research typically avoided due to financial constraints. The decentralized network taps into a global talent pool of miners, fostering a hyper-competitive yet collaborative environment. Amanda explains, "The merit-based like hyper-competitive model also means that miners are constantly optimizing for the shortest path to a reward without the bias of a medicinal chemist." This adversarial setting stress-tests AI models, revealing weaknesses crucial for improving predictive accuracy.

• Strategic Insight: Bittensor's model provides non-dilutive funding (via token emissions) and incentivizes rapid, unbiased model optimization, potentially overcoming traditional R&D bottlenecks. Investors should note this novel approach to funding high-risk, early-stage biotech research.

Nova's Technical Mechanism: Predicting Molecular Interactions

Nova utilizes a massive dataset from Savvy containing over a billion synthesizable molecules – a key distinction from theoretical molecule datasets, making findings more actionable. Miners download this dataset (over half a million downloads noted in the past month) and use it to predict the binding affinity between molecules and specific protein targets/anti-targets.

• Binding Affinity: A measure of how strongly a molecule binds to a protein target. High affinity for a target protein (involved in a disease) and low affinity for anti-targets (proteins that could cause side effects) is desirable.

Validators use a state-of-the-art prediction model (later identified as Psychic) to score miner submissions and identify winners. The generated data informs chemical maps, libraries of promising molecules, model fine-tuning, and future real-world synthesis and validation efforts.

Iterative Refinement: Learning from Miner Behavior

Since launching on March 1st, Nova has run ~780 challenges exploring ~4,000 proteins, generating ~17,000 unique molecule predictions. Pedro details the rapid iteration of their incentive mechanism based on miners efficiently finding "paths of least resistance." Initial challenges involved a single target, evolving to target/anti-target pairs, and finally to predicting affinity for one target against a set of anti-targets. They implemented encrypted submissions to prevent copying and expanded the protein dataset to over 2 million to increase unpredictability. Adjustments included setting minimum requirements for heavy atoms (atoms other than hydrogen, relevant for molecular complexity) and continuously tuning scoring parameters.

• Actionable Insight: The team's rapid iteration demonstrates the dynamic nature of decentralized AI networks. Researchers can observe how incentive design directly shapes miner behavior and the resulting data quality. Investors should appreciate the team's adaptability in refining the system.

Uncovering Model Limitations: The Power of Adversarial Mining

Pedro explains how miners quickly identified weaknesses or "holes" in the Psychic prediction model, areas not typically reported in academic literature. Initially, miners found that molecules with very low atom counts, though rare in the Savvy dataset, often yielded high predicted affinity scores, indicating a region of low confidence or high variance in the model. The team implemented countermeasures like minimum atom counts and the target/anti-target system. However, miners then discovered another loophole: rigid molecules with a high number of rotatable bonds (bonds allowing parts of a molecule to rotate freely, affecting flexibility and interaction potential) but low actual rotation could also win challenges, again highlighting an underrepresented molecular type where the model might be less reliable, especially for predicting selectivity.

• Key Takeaway: Bittensor's adversarial environment serves as an effective, real-time stress test for AI models, uncovering limitations that traditional validation might miss. This process generates valuable data on model uncertainty.

The Shannon Upgrade: Enhancing Diversity and Exploration

The upcoming "Shannon Upgrade" (scheduled for April 11th) directly addresses the findings from miner behavior. It retains the one target vs. four anti-targets format (one fixed weekly target for library building, four random anti-targets as proxies for off-target effects). Key changes include requiring miners to submit sets of molecules, penalizing repetition, and introducing a diversity bonus calculated using Shannon entropy (a measure of diversity or unpredictability) on the submitted molecules' representations. The goal is to incentivize broader exploration of the chemical space and map regions where the prediction model has high variance or uncertainty.

• Strategic Implication: This upgrade aims to leverage miner ingenuity not just for optimization but for systematic exploration and mapping of the model's weaknesses across the chemical space, generating a unique dataset on model reliability.

Strategic Focus: The "Treat" Initiative and Metaprogramming Drugs

Amanda introduces Nova's first major mapping project: "Treat" (Targeted Reward Evaluation and Therapeutics). This focuses on protein targets associated with reward, learning, and behavioral regulation (e.g., focus, eating, sleep, trauma). The goal is to develop "metaprogramming drugs"—compounds that help individuals regulate their own behaviors, aligning with evidence suggesting lifestyle modifications (like caloric restriction, sleep hygiene) are powerful health interventions. While seemingly abstract, this translates to therapeutic areas like ADHD and sexual dysfunction (e.g., Adderall, Viagra), but with the aim of developing better solutions.

• Research Angle: This initiative represents a novel framing for drug discovery, focusing on behavioral self-regulation rather than solely disease treatment. It connects computational drug discovery with behavioral science and longevity research.

Future Vision: Building a Digital-to-Real-World Flywheel

Nova aims to be more than just the subnet; it aspires to be the seed for a "crypto-native biotech company." The plan involves creating a flywheel: using the subnet's outputs for their own R&D, partnering with external companies and universities (conversations are active, one potential partner already interested), and eventually validating top computational predictions through real-world chemical synthesis and wet lab assays. This validation data will feed back into the subnet, improving models and creating increasingly valuable datasets and drug candidates. They are also working on integrating predictions for other crucial drug properties like ADME/Tox (Absorption, Distribution, Metabolism, Excretion, and Toxicity) and blood-brain barrier permeability.

• Investor Note: The long-term strategy involves bridging the gap between computational prediction and tangible, validated drug assets, creating multiple potential revenue streams from data licensing, partnerships, and proprietary drug development.

Deep Dive: Nova's Validation and Verification System

Responding to Miko's query, Amanda reiterates the core challenge: miners submit molecules predicted to maximize binding affinity to a target protein while minimizing affinity to anti-target proteins, using the Psychic model for scoring. This process simulates the search for selective drugs – compounds that hit the desired biological target without causing side effects via off-target interactions. The goal is to find molecules selective for desired effects (e.g., activating GABA receptors for neurological benefits) without hitting unwanted targets.

The Importance of Selectivity and Problem Scale

Selectivity is crucial; many drugs fail in clinical trials due to unforeseen interactions with off-targets causing side effects. Pedro emphasizes that finding molecules that bind only to the intended target is paramount. The computational challenge is immense due to the vastness of the chemical space (billions or trillions of potential molecules). Nova aims to accelerate this search dramatically. Pedro states that traditional large-scale in silico (computational) screening can take weeks or months, suggesting Bittensor's speed offers a significant advantage. Greater chemical diversity in potential hits increases the chances of finding a viable drug candidate.

Economic Implications: The Value Proposition for Pharma

The team estimates that pharmaceutical companies spend hundreds of millions on computational screening. Speeding up this process and improving its accuracy (narrowing the top of the development "funnel") offers massive cost savings, given the $2.6 billion average cost per approved drug is largely due to failures along the way. Amanda contrasts Nova's open, incentive-driven approach with traditional pharma R&D, where companies often keep models and data private, limiting collective progress. Bittensor incentivizes data sharing and collaborative model improvement.

• Actionable Insight: Nova's potential to drastically reduce the time and cost of early-stage drug discovery presents a compelling value proposition for the pharmaceutical industry, potentially disrupting existing R&D service models.

Miner Strategies and the Path to Model Improvement

Pedro speculates that miners likely use a mix of large-scale prediction runs and deep exploratory data analysis to identify patterns and model weaknesses quickly. The "Shannon Upgrade" aims to push miners beyond exploiting known loopholes towards broader exploration. The data generated, particularly on model failures and high-variance regions, is invaluable. Amanda explains this data can be used to fine-tune Psychic (or other models) for specific targets or drug-like properties, potentially creating superior, specialized models trained on Bittensor data, including future wet lab validation results.

Expanding Capabilities: Composability and Future Models

Pedro confirms Nova's modular design allows for incorporating additional models beyond Psychic. They are developing a model for blood-brain barrier permeability (crucial for psychiatric drugs targeted by "Treat") and plan to add models for other pharmacokinetic/pharmacodynamic properties. Miko highlights that this composability means Nova could optimize any underfunded open-source model, using miner activity to expose weaknesses and drive improvement, creating a new generation of robust, openly trained models.

Nova's Long-Term Goals and Monetization Strategy

The team sees the subnet as a portal enabling both their internal R&D and external use. Monetization will likely start with partnerships (a service model) and potentially evolve to token-gated access to curated chemical libraries or licensing validated drug candidates. Pedro emphasizes leveraging his industry network for partnerships. The value increases exponentially as candidates move further down the development path and gain real-world validation.

Navigating Open Data and Industry Collaboration

Addressing concerns about open-sourcing data potentially sensitive to partners, Pedro suggests that significant analysis and interpretation are still required after the initial screening, providing opportunities for proprietary value creation on top of the open outputs. While encryption is possible on Bittensor, the team seems focused on demonstrating value openly first. Amanda frames it as bringing disparate industries together, arguing crypto's transparency and incentive models can address pharma's innovation crisis. Partnerships mentioned (though unnamed due to confidentiality) include a research university, a private company, and a CRO (Contract Research Organization – a company providing outsourced research services, often lab work).

Addressing Core Pharma Challenges via Decentralization

Amanda reiterates the core problems Nova tackles: the prohibitive time and cost of drug development, the resulting lack of novelty, and the misaligned incentives that hinder exploration of truly innovative therapies. Biotech VCs often demand clinical trial data, creating a funding gap for early-stage, high-risk research – a gap Bittensor's tokenomics can potentially fill. The decentralized, open nature aims to break down data silos and foster collaborative innovation.

Reflective and Strategic Conclusion

Nova exemplifies Bittensor's potential to apply decentralized AI and crypto-economics to complex, real-world scientific challenges like drug discovery. Investors should monitor Nova's progress in generating validated results and securing partnerships, while researchers can observe a unique paradigm for open, incentivized stress-testing and improvement of AI models.