With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
NOVA (Bittensor Subnet 68) is a specialized decentralized AI network focused on early-stage drug discovery. Developed by Metanova Labs, NOVA serves as a global open drug screening platform powered by the Bittensor blockchain. Its mission is to dramatically speed up and democratize the search for new therapeutics by crowdsourcing AI models and computation. Traditional drug R&D is slow, costly, and risky, often taking over a decade and billions of dollars for a single drug with >90% failure rates. NOVA addresses these inefficiencies by reframing drug discovery as a high-speed, distributed optimization problem.
In NOVA’s network, participants worldwide contribute models and compute to rapidly screen an enormous chemical space (on the order of 10^60 molecules) for promising drug candidates. Every participant – whether a “miner” generating molecule candidates or a “validator” evaluating them – helps collectively explore a billion-scale virtual library of compounds in parallel, optimizing for molecules most likely to bind a given biological target. In essence, NOVA is a “decentralized engine for drug discovery”, transforming virtual drug screening into a competitive, accelerated race for breakthroughs that leverages global intelligence and rewards contributors for innovative solutions
NOVA (Bittensor Subnet 68) is a specialized decentralized AI network focused on early-stage drug discovery. Developed by Metanova Labs, NOVA serves as a global open drug screening platform powered by the Bittensor blockchain. Its mission is to dramatically speed up and democratize the search for new therapeutics by crowdsourcing AI models and computation. Traditional drug R&D is slow, costly, and risky, often taking over a decade and billions of dollars for a single drug with >90% failure rates. NOVA addresses these inefficiencies by reframing drug discovery as a high-speed, distributed optimization problem.
In NOVA’s network, participants worldwide contribute models and compute to rapidly screen an enormous chemical space (on the order of 10^60 molecules) for promising drug candidates. Every participant – whether a “miner” generating molecule candidates or a “validator” evaluating them – helps collectively explore a billion-scale virtual library of compounds in parallel, optimizing for molecules most likely to bind a given biological target. In essence, NOVA is a “decentralized engine for drug discovery”, transforming virtual drug screening into a competitive, accelerated race for breakthroughs that leverages global intelligence and rewards contributors for innovative solutions
Nova’s product is a decentralized platform that uses machine learning to accelerate drug discovery. It connects a large pool of miners to analyze and optimize vast datasets of chemical compounds. The miners engage in challenges where they predict how different molecules will interact with proteins (target and anti-target), and this process is validated by a state-of-the-art AI model called “Psychic.” Through iterative challenges and optimization, Nova can find molecules with the highest affinity for specific targets while minimizing unwanted interactions, thus reducing side effects. The subnet focuses on chemical libraries, fine-tuning models, synthesizing molecules, and validating them in real-world environments, which makes the platform robust for both its own research and collaborations with other institutions and companies.
The subnet uses:
The subnet’s architecture consists of three primary components:
Miners – These are nodes that generate and propose candidate molecules. Each miner runs specialized search algorithms over a vast chemical library (the SAVI-2020 database of ~1.75 billion synthesizable compounds) to find molecules predicted to bind strongly to the current target protein. Miners can use machine learning models (e.g. QSAR or deep learning) and heuristic methods (like active learning or substructure-based search) to intelligently navigate chemical space. Notably, miners continuously refine their search using feedback from the network’s oracle model (described below), focusing on higher-scoring regions of chemical space rather than brute-forcing all possibilities. Each miner can only hold one “active” molecule submission at a time, and is incentivized to update it only when they discover a better candidate.
Validators – These are nodes that evaluate the quality of miners’ submissions using a predefined incentive mechanism. In NOVA, the validators all run a Deterministic Oracle model (called PSICHIC) to score each submitted molecule’s binding affinity to the target protein. Essentially, validators act as judges: at the end of every block (each block ≈12 seconds in Bittensor), they compute a binding affinity score for each miner’s current molecule using the oracle and collectively rank the miners. Because PSICHIC is a fixed, open-source model that gives deterministic predictions for a given protein–molecule pair, all validators will obtain the same score for the same input, ensuring objective and transparent evaluation across the network. This removes subjectivity – the oracle’s score serves as a ground-truth standard that validators enforce uniformly. The top-scoring submission is identified each block, and validators submit those results to the blockchain (via Bittensor’s consensus mechanism) to determine reward distribution.
Deterministic Oracle (PSICHIC) – PSICHIC is the pretrained predictive model at the heart of NOVA’s evaluation process. It is a state-of-the-art protein–ligand binding affinity model that takes a protein’s sequence (or structure) and a molecule (e.g. SMILES representation) and outputs a binding affinity score. Because PSICHIC’s outputs are reproducible and deterministic (given a fixed model version and input), it provides a reliable yardstick for comparing candidates. In NOVA’s “competition,” PSICHIC essentially plays the role of an automated in silico screening assay – it provides the scoring function (analogous to a loss function in ML terms) that all miners are trying to maximize. By anchoring the incentive mechanism to an open and fixed oracle, NOVA ensures a level playing field: the only goal is to find molecules that score higher by PSICHIC’s metric, and everyone knows the metric in advance.
Network process: NOVA operates in iterative challenge rounds targeting specific drug discovery problems. In NOVA v1, each challenge is defined by a single protein target – miners must find a molecule from the database with the highest predicted affinity to that target. All miners start searching as soon as a new challenge (target) is announced, and they continuously submit molecules over the round (which spans roughly 360 blocks, ≈1 hour in the current design). Importantly, submissions are updated on the fly: if a miner discovers a better candidate, it replaces their previous one (ensuring that at any time, each miner only occupies one “slot” in the competition). Every block, validators evaluate all current submissions using PSICHIC and determine that block’s top scorer. This creates a fast-paced, block-by-block tournament: “a high-speed race for breakthroughs.” The miner whose molecule yields the highest affinity score (if higher than all peers that block) is declared the winner of that block’s sub-challenge. Ties are broken by whoever submitted the winning molecule first.
All evaluated scores and winning molecules are recorded, and a leaderboard of results is made visible to the community in real time. This transparency lets all participants learn which molecular strategies are working best, encouraging adaptive competition – miners can observe what kinds of molecules are scoring well and adjust their search accordingly in subsequent submissions. At the end of the round (e.g. after 360 blocks), the challenge concludes and a new protein target can be selected for the next cycle. Over time, NOVA plans to increase the complexity of these challenges – moving from single-target affinity optimization to multi-objective searches that consider multiple protein targets or additional drug-like properties (such as toxicity, metabolic stability, etc.). This will make the subnet’s task more closely resemble real drug discovery, which requires balancing many factors for a viable drug candidate.
Role within the Bittensor Ecosystem
NOVA is one of many specialized subnets in the broader Bittensor ecosystem. Each subnet in Bittensor is essentially an independent “market” for a particular AI service or commodity, running on the shared Bittensor blockchain (often called Subtensor). While some subnets focus on language modeling, data curation, or other AI tasks, Subnet 68 (NOVA) is uniquely dedicated to decentralized drug discovery. For context, other notable subnets include SN9 (Pretrain) for large-scale language model pre-training, SN13 (Data Universe) for data collection, and SN25 (Folding) for protein folding simulations. NOVA complements this ecosystem by tackling the domain of medicinal chemistry and DeSci (decentralized science) — it brings a biotech use-case to Bittensor’s AI network.
Because all subnets share the TAO token and core blockchain, participants can seamlessly move between subnets or even contribute to multiple ones in parallel. In NOVA’s case, this opens the door for cross-subnet collaboration. For example, in the future NOVA could integrate insights from other subnets: a language-model subnet might help generate novel molecular structures, a protein-folding subnet (like SN25) could provide protein 3D structures to improve scoring, or a data subnet could supply biochemical data to refine models. The NOVA whitepaper explicitly notes the potential synergy of NOVA with subnets like SN9, SN13, and SN25, creating a “synergistic ecosystem” of AI services that feed into each other. This modular design is a strength of Bittensor – each subnet can specialize but also benefit from others’ outputs, all under a unified economic system.
Operationally, NOVA uses Bittensor’s infrastructure for security and consensus. Bittensor employs a consensus algorithm (Yuma) whereby validators’ reported performance scores determine how new tokens (“emissions”) are distributed to miners. Each subnet creator (in this case, Metanova Labs for NOVA) defines the incentive mechanism that validators must apply. NOVA’s incentive mechanism is the PSICHIC affinity score – validators simply use that to grade miners. Thus, NOVA slots into Bittensor’s framework: miners and validators join by paying a TAO registration fee to get a seat (UID) in Subnet 68, and then run NOVA’s open-source code (miners run search algorithms, validators run the oracle scoring code). The subnet can accommodate up to 256 active participants (max 192 miners + 64 validators, per Bittensor’s design limits), ensuring a healthy amount of decentralization while preventing the challenge from being flooded with low-quality contributions. Bittensor’s base layer handles block production, token transfers, staking, and registration, so NOVA’s creators could focus on the domain-specific logic (drug screening and reward rules). In summary, NOVA operates as a plug-in module on Bittensor – it leverages the network’s global decentralized compute power and token incentives to tackle a very specialized AI problem (virtual drug discovery) that would be impossible to crowdsource at this scale without blockchain coordination.
Unique Features and Innovations
NOVA introduces several innovative elements that set it apart from traditional drug discovery approaches and even other AI networks:
Decentralized & Democratized R&D: NOVA is arguably the first open, permissionless drug discovery engine. Anyone with the requisite expertise and compute can become a drug-hunting miner or a validator, regardless of institutional affiliation. This model crowdsources ideas and approaches from around the world, breaking the siloed nature of pharma R&D. It also means progress in NOVA is transparent – every winning molecule and score is visible on-chain, creating an open repository of potential drug candidates over time. This transparency and community access to results is a departure from the secretive, proprietary research model.
Gamified “Search Economy”: By structuring drug discovery as a competitive game with frequent rewards, NOVA incentivizes rapid innovation. The “high-speed race” format (12-second rounds, continuous leaderboard updates) turns what is normally a painstaking lab process into something dynamic and engaging. Researchers are encouraged to try bold or diverse strategies since only the top result matters per round – exploration of chemical space is rewarded. This game-theoretic approach, powered by crypto incentives, is novel in scientific research. It aligns individual profit-seeking with a collective goal (finding better drugs).
Objective Oracle & Fair Evaluation: The use of a fixed, state-of-the-art ML model (PSICHIC) as a deterministic oracle is a unique innovation. It provides an objective, reproducible benchmark for all contributions. All miners effectively optimize the same known function, and all validators enforce that function. This avoids human bias or subjective judging of results – something quite important in science competitions. It’s akin to a leaderboard challenge in Kaggle or other ML competitions, but running continuously on-chain with automatic scoring. The choice of PSICHIC (which is high-performing in binding affinity prediction) ensures the evaluations are scientifically meaningful. Over time, NOVA can upgrade or fine-tune the oracle (with community governance) as better models become available, always maintaining a single source of truth for scoring.
Massive Search Space & Dataset: NOVA leverages the SAVI-2020 library (1.75 billion synthesizable molecules) as its search domain. This is orders of magnitude larger than what any single lab could screen experimentally. By encoding this huge chemical space into a distributed search problem, NOVA pushes the boundary of what virtual screening can do. Moreover, every molecule in SAVI-2020 comes with known synthetic routes (it’s “make-able” chemistry). This means any top hits NOVA finds are not just theoretical – they can be synthesized in real life, greatly increasing the impact of the computational results. The focus on synthesizability and drug-likeness (SAVI compounds are built from known reactions) gives NOVA an edge in producing drug candidates that are realistic, not just random molecules. This is a deliberate innovation to bridge computation and wet-lab applicability.
Integration of DeSci and Web3: NOVA is at the intersection of decentralized science and blockchain. By building on Bittensor, it integrates with a wider Web3 ecosystem – for instance, results could be NFT-encoded or used in decentralized biotech funding platforms down the line. The project has garnered interest from both the AI community and the DeSci community (followers include prominent DeSci figures) because it demonstrates a working model of crypto-driven scientific research. The entire pipeline from data (open dataset on HuggingFace
X.COM) to model (PSICHIC on HuggingFace) to code (open-source on GitHub) to results (blockchain explorer) is open and transparent. This kind of end-to-end openness is an innovative paradigm for drug discovery, which historically has been proprietary.
Future Real-World Impact: A key planned innovation is to close the loop with real experiments. NOVA’s roadmap includes taking the highest-scoring molecules from the network (the “virtual hits”) and synthesizing/testing them in actual laboratories (wet lab validation). The results of those biological assays would then be fed back into the network – for example, to retrain or recalibrate the oracle model (making PSICHIC more accurate in future rounds). This creates a powerful virtuous cycle between decentralized computation and centralized experimentation. If successful, NOVA could not only propose drug candidates but also verify them, leading to a pipeline of potentially patentable or development-ready compounds emerging from a decentralized community. Such an integration of on-chain and off-chain research is highly unique. Additionally, NOVA envisions tokenizing libraries of top molecules – essentially creating crypto assets representing promising compounds, which could be traded or funded for further development. This would bring liquidity and market dynamics to early-stage drug assets, a radical innovation in how drug discovery projects are valued and advanced.
Nova’s product is a decentralized platform that uses machine learning to accelerate drug discovery. It connects a large pool of miners to analyze and optimize vast datasets of chemical compounds. The miners engage in challenges where they predict how different molecules will interact with proteins (target and anti-target), and this process is validated by a state-of-the-art AI model called “Psychic.” Through iterative challenges and optimization, Nova can find molecules with the highest affinity for specific targets while minimizing unwanted interactions, thus reducing side effects. The subnet focuses on chemical libraries, fine-tuning models, synthesizing molecules, and validating them in real-world environments, which makes the platform robust for both its own research and collaborations with other institutions and companies.
The subnet uses:
The subnet’s architecture consists of three primary components:
Miners – These are nodes that generate and propose candidate molecules. Each miner runs specialized search algorithms over a vast chemical library (the SAVI-2020 database of ~1.75 billion synthesizable compounds) to find molecules predicted to bind strongly to the current target protein. Miners can use machine learning models (e.g. QSAR or deep learning) and heuristic methods (like active learning or substructure-based search) to intelligently navigate chemical space. Notably, miners continuously refine their search using feedback from the network’s oracle model (described below), focusing on higher-scoring regions of chemical space rather than brute-forcing all possibilities. Each miner can only hold one “active” molecule submission at a time, and is incentivized to update it only when they discover a better candidate.
Validators – These are nodes that evaluate the quality of miners’ submissions using a predefined incentive mechanism. In NOVA, the validators all run a Deterministic Oracle model (called PSICHIC) to score each submitted molecule’s binding affinity to the target protein. Essentially, validators act as judges: at the end of every block (each block ≈12 seconds in Bittensor), they compute a binding affinity score for each miner’s current molecule using the oracle and collectively rank the miners. Because PSICHIC is a fixed, open-source model that gives deterministic predictions for a given protein–molecule pair, all validators will obtain the same score for the same input, ensuring objective and transparent evaluation across the network. This removes subjectivity – the oracle’s score serves as a ground-truth standard that validators enforce uniformly. The top-scoring submission is identified each block, and validators submit those results to the blockchain (via Bittensor’s consensus mechanism) to determine reward distribution.
Deterministic Oracle (PSICHIC) – PSICHIC is the pretrained predictive model at the heart of NOVA’s evaluation process. It is a state-of-the-art protein–ligand binding affinity model that takes a protein’s sequence (or structure) and a molecule (e.g. SMILES representation) and outputs a binding affinity score. Because PSICHIC’s outputs are reproducible and deterministic (given a fixed model version and input), it provides a reliable yardstick for comparing candidates. In NOVA’s “competition,” PSICHIC essentially plays the role of an automated in silico screening assay – it provides the scoring function (analogous to a loss function in ML terms) that all miners are trying to maximize. By anchoring the incentive mechanism to an open and fixed oracle, NOVA ensures a level playing field: the only goal is to find molecules that score higher by PSICHIC’s metric, and everyone knows the metric in advance.
Network process: NOVA operates in iterative challenge rounds targeting specific drug discovery problems. In NOVA v1, each challenge is defined by a single protein target – miners must find a molecule from the database with the highest predicted affinity to that target. All miners start searching as soon as a new challenge (target) is announced, and they continuously submit molecules over the round (which spans roughly 360 blocks, ≈1 hour in the current design). Importantly, submissions are updated on the fly: if a miner discovers a better candidate, it replaces their previous one (ensuring that at any time, each miner only occupies one “slot” in the competition). Every block, validators evaluate all current submissions using PSICHIC and determine that block’s top scorer. This creates a fast-paced, block-by-block tournament: “a high-speed race for breakthroughs.” The miner whose molecule yields the highest affinity score (if higher than all peers that block) is declared the winner of that block’s sub-challenge. Ties are broken by whoever submitted the winning molecule first.
All evaluated scores and winning molecules are recorded, and a leaderboard of results is made visible to the community in real time. This transparency lets all participants learn which molecular strategies are working best, encouraging adaptive competition – miners can observe what kinds of molecules are scoring well and adjust their search accordingly in subsequent submissions. At the end of the round (e.g. after 360 blocks), the challenge concludes and a new protein target can be selected for the next cycle. Over time, NOVA plans to increase the complexity of these challenges – moving from single-target affinity optimization to multi-objective searches that consider multiple protein targets or additional drug-like properties (such as toxicity, metabolic stability, etc.). This will make the subnet’s task more closely resemble real drug discovery, which requires balancing many factors for a viable drug candidate.
Role within the Bittensor Ecosystem
NOVA is one of many specialized subnets in the broader Bittensor ecosystem. Each subnet in Bittensor is essentially an independent “market” for a particular AI service or commodity, running on the shared Bittensor blockchain (often called Subtensor). While some subnets focus on language modeling, data curation, or other AI tasks, Subnet 68 (NOVA) is uniquely dedicated to decentralized drug discovery. For context, other notable subnets include SN9 (Pretrain) for large-scale language model pre-training, SN13 (Data Universe) for data collection, and SN25 (Folding) for protein folding simulations. NOVA complements this ecosystem by tackling the domain of medicinal chemistry and DeSci (decentralized science) — it brings a biotech use-case to Bittensor’s AI network.
Because all subnets share the TAO token and core blockchain, participants can seamlessly move between subnets or even contribute to multiple ones in parallel. In NOVA’s case, this opens the door for cross-subnet collaboration. For example, in the future NOVA could integrate insights from other subnets: a language-model subnet might help generate novel molecular structures, a protein-folding subnet (like SN25) could provide protein 3D structures to improve scoring, or a data subnet could supply biochemical data to refine models. The NOVA whitepaper explicitly notes the potential synergy of NOVA with subnets like SN9, SN13, and SN25, creating a “synergistic ecosystem” of AI services that feed into each other. This modular design is a strength of Bittensor – each subnet can specialize but also benefit from others’ outputs, all under a unified economic system.
Operationally, NOVA uses Bittensor’s infrastructure for security and consensus. Bittensor employs a consensus algorithm (Yuma) whereby validators’ reported performance scores determine how new tokens (“emissions”) are distributed to miners. Each subnet creator (in this case, Metanova Labs for NOVA) defines the incentive mechanism that validators must apply. NOVA’s incentive mechanism is the PSICHIC affinity score – validators simply use that to grade miners. Thus, NOVA slots into Bittensor’s framework: miners and validators join by paying a TAO registration fee to get a seat (UID) in Subnet 68, and then run NOVA’s open-source code (miners run search algorithms, validators run the oracle scoring code). The subnet can accommodate up to 256 active participants (max 192 miners + 64 validators, per Bittensor’s design limits), ensuring a healthy amount of decentralization while preventing the challenge from being flooded with low-quality contributions. Bittensor’s base layer handles block production, token transfers, staking, and registration, so NOVA’s creators could focus on the domain-specific logic (drug screening and reward rules). In summary, NOVA operates as a plug-in module on Bittensor – it leverages the network’s global decentralized compute power and token incentives to tackle a very specialized AI problem (virtual drug discovery) that would be impossible to crowdsource at this scale without blockchain coordination.
Unique Features and Innovations
NOVA introduces several innovative elements that set it apart from traditional drug discovery approaches and even other AI networks:
Decentralized & Democratized R&D: NOVA is arguably the first open, permissionless drug discovery engine. Anyone with the requisite expertise and compute can become a drug-hunting miner or a validator, regardless of institutional affiliation. This model crowdsources ideas and approaches from around the world, breaking the siloed nature of pharma R&D. It also means progress in NOVA is transparent – every winning molecule and score is visible on-chain, creating an open repository of potential drug candidates over time. This transparency and community access to results is a departure from the secretive, proprietary research model.
Gamified “Search Economy”: By structuring drug discovery as a competitive game with frequent rewards, NOVA incentivizes rapid innovation. The “high-speed race” format (12-second rounds, continuous leaderboard updates) turns what is normally a painstaking lab process into something dynamic and engaging. Researchers are encouraged to try bold or diverse strategies since only the top result matters per round – exploration of chemical space is rewarded. This game-theoretic approach, powered by crypto incentives, is novel in scientific research. It aligns individual profit-seeking with a collective goal (finding better drugs).
Objective Oracle & Fair Evaluation: The use of a fixed, state-of-the-art ML model (PSICHIC) as a deterministic oracle is a unique innovation. It provides an objective, reproducible benchmark for all contributions. All miners effectively optimize the same known function, and all validators enforce that function. This avoids human bias or subjective judging of results – something quite important in science competitions. It’s akin to a leaderboard challenge in Kaggle or other ML competitions, but running continuously on-chain with automatic scoring. The choice of PSICHIC (which is high-performing in binding affinity prediction) ensures the evaluations are scientifically meaningful. Over time, NOVA can upgrade or fine-tune the oracle (with community governance) as better models become available, always maintaining a single source of truth for scoring.
Massive Search Space & Dataset: NOVA leverages the SAVI-2020 library (1.75 billion synthesizable molecules) as its search domain. This is orders of magnitude larger than what any single lab could screen experimentally. By encoding this huge chemical space into a distributed search problem, NOVA pushes the boundary of what virtual screening can do. Moreover, every molecule in SAVI-2020 comes with known synthetic routes (it’s “make-able” chemistry). This means any top hits NOVA finds are not just theoretical – they can be synthesized in real life, greatly increasing the impact of the computational results. The focus on synthesizability and drug-likeness (SAVI compounds are built from known reactions) gives NOVA an edge in producing drug candidates that are realistic, not just random molecules. This is a deliberate innovation to bridge computation and wet-lab applicability.
Integration of DeSci and Web3: NOVA is at the intersection of decentralized science and blockchain. By building on Bittensor, it integrates with a wider Web3 ecosystem – for instance, results could be NFT-encoded or used in decentralized biotech funding platforms down the line. The project has garnered interest from both the AI community and the DeSci community (followers include prominent DeSci figures) because it demonstrates a working model of crypto-driven scientific research. The entire pipeline from data (open dataset on HuggingFace
X.COM) to model (PSICHIC on HuggingFace) to code (open-source on GitHub) to results (blockchain explorer) is open and transparent. This kind of end-to-end openness is an innovative paradigm for drug discovery, which historically has been proprietary.
Future Real-World Impact: A key planned innovation is to close the loop with real experiments. NOVA’s roadmap includes taking the highest-scoring molecules from the network (the “virtual hits”) and synthesizing/testing them in actual laboratories (wet lab validation). The results of those biological assays would then be fed back into the network – for example, to retrain or recalibrate the oracle model (making PSICHIC more accurate in future rounds). This creates a powerful virtuous cycle between decentralized computation and centralized experimentation. If successful, NOVA could not only propose drug candidates but also verify them, leading to a pipeline of potentially patentable or development-ready compounds emerging from a decentralized community. Such an integration of on-chain and off-chain research is highly unique. Additionally, NOVA envisions tokenizing libraries of top molecules – essentially creating crypto assets representing promising compounds, which could be traded or funded for further development. This would bring liquidity and market dynamics to early-stage drug assets, a radical innovation in how drug discovery projects are valued and advanced.
NOVA is a project by Metanova Labs, a crypto-native biotechnology company spearheading the fusion of AI drug discovery with decentralized networks. The Nova subnet team is composed of experts in the fields of AI, machine learning, and drug discovery. They combine their experience in these domains to create a decentralized platform that can help solve inefficiencies in traditional drug development processes. The team includes:
Micaela Bazo – CEO
Pedro Penna – CSO
Amanda Casadei – CTO
Brayden Miller – Engineer
NOVA is a project by Metanova Labs, a crypto-native biotechnology company spearheading the fusion of AI drug discovery with decentralized networks. The Nova subnet team is composed of experts in the fields of AI, machine learning, and drug discovery. They combine their experience in these domains to create a decentralized platform that can help solve inefficiencies in traditional drug development processes. The team includes:
Micaela Bazo – CEO
Pedro Penna – CSO
Amanda Casadei – CTO
Brayden Miller – Engineer
Since its launch in early 2025 (announced as live on Subnet 68 in March 2025), NOVA has rolled out Version 1.0 of its platform focusing on single-target affinity optimization. The key phases of the roadmap include:
Incentive Mechanism Refinement: The team has iterated on their incentive mechanism to ensure that miners stay focused on drug discovery, rather than blockchain-centric issues. They’ve implemented features like encrypted submissions, anti-target proteins, and penalties for repetitive submissions to guide miners toward more diverse and valuable results.
Upgrades and New Challenges: Nova launched several upgrades to its challenge system, including the Shannon upgrade, which encourages more diverse molecule exploration and targets regions of high variance in the prediction model. The team also introduced the “Treat” challenge to focus on targets related to reward and learning mechanisms—drugs that could impact behavior, such as focus and sleep regulation.
Real-World Validation: The team is working on the next step of the roadmap, which involves synthesizing and validating the predicted molecules in real-world settings through laboratory tests and biological assays. This will help verify the predictions made by the AI models and enhance the platform’s credibility.
Partnership Development: Nova is building strategic partnerships with universities, private companies, and CROs to enhance its data capabilities and apply its solutions to real-world pharmaceutical problems. These partnerships will help Nova scale its solutions and bring them to market.
New Prediction Models: The team is actively developing new models, such as a blood-brain barrier permeability model, which is essential for developing psychiatric drugs. Additionally, they are exploring models that predict pharmacokinetics and pharmacodynamics to further optimize drug candidates.
Monetization and Broader Applications: Once Nova validates its predictions with real-world data, it plans to monetize its chemical libraries and prediction capabilities through token-gated access. Nova will also offer its services to pharmaceutical companies, universities, and researchers interested in accelerating their drug discovery efforts.
In summary, Nova aims to create a decentralized drug discovery platform that not only accelerates research but also helps bring innovative drugs to market faster, all while leveraging the power of the Bittensor network. The subnet’s open-source nature and incentive mechanisms ensure a collaborative and rapidly evolving environment, setting the stage for the next breakthrough in pharmaceutical science.
Since its launch in early 2025 (announced as live on Subnet 68 in March 2025), NOVA has rolled out Version 1.0 of its platform focusing on single-target affinity optimization. The key phases of the roadmap include:
Incentive Mechanism Refinement: The team has iterated on their incentive mechanism to ensure that miners stay focused on drug discovery, rather than blockchain-centric issues. They’ve implemented features like encrypted submissions, anti-target proteins, and penalties for repetitive submissions to guide miners toward more diverse and valuable results.
Upgrades and New Challenges: Nova launched several upgrades to its challenge system, including the Shannon upgrade, which encourages more diverse molecule exploration and targets regions of high variance in the prediction model. The team also introduced the “Treat” challenge to focus on targets related to reward and learning mechanisms—drugs that could impact behavior, such as focus and sleep regulation.
Real-World Validation: The team is working on the next step of the roadmap, which involves synthesizing and validating the predicted molecules in real-world settings through laboratory tests and biological assays. This will help verify the predictions made by the AI models and enhance the platform’s credibility.
Partnership Development: Nova is building strategic partnerships with universities, private companies, and CROs to enhance its data capabilities and apply its solutions to real-world pharmaceutical problems. These partnerships will help Nova scale its solutions and bring them to market.
New Prediction Models: The team is actively developing new models, such as a blood-brain barrier permeability model, which is essential for developing psychiatric drugs. Additionally, they are exploring models that predict pharmacokinetics and pharmacodynamics to further optimize drug candidates.
Monetization and Broader Applications: Once Nova validates its predictions with real-world data, it plans to monetize its chemical libraries and prediction capabilities through token-gated access. Nova will also offer its services to pharmaceutical companies, universities, and researchers interested in accelerating their drug discovery efforts.
In summary, Nova aims to create a decentralized drug discovery platform that not only accelerates research but also helps bring innovative drugs to market faster, all while leveraging the power of the Bittensor network. The subnet’s open-source nature and incentive mechanisms ensure a collaborative and rapidly evolving environment, setting the stage for the next breakthrough in pharmaceutical science.
A big thank you to Tao Stats for producing these insightful videos in the Novelty Search series. We appreciate the opportunity to dive deep into the groundbreaking work being done by Subnets within Bittensor! Check out some of their other videos HERE.
In this session, the team behind the Nova subnet, discuss how they are revolutionizing drug discovery by leveraging decentralized AI. Their goal is to optimize the early stages of drug development by predicting the binding affinity of molecules to specific protein targets while minimizing unwanted side effects. They explore how their incentive-driven model encourages miners to rapidly test and optimize chemical compounds, uncovering novel molecules that can drive breakthroughs in pharmaceuticals. By combining AI, large-scale data, and decentralized computing, Nova aims to drastically reduce the time and cost associated with drug development, while making it more accessible to researchers and pharmaceutical companies worldwide.
Novelty Search is great, but for most investors trying to understand Bittensor, the technical depth is a wall, not a bridge. If we’re going to attract investment into this ecosystem then we need more people to understand it! That’s why Siam Kidd and Mark Creaser from DSV Fund have launched Revenue Search, where they ask the simple questions that investors want to know the answers to.
Recorded in July 2025, this episode of Revenue Search features Michaela, founder of Metanova, the first decentralized drug discovery subnet on Bittensor. Metanova uses AI to screen billions of synthesizable molecules for potential therapeutic use—dramatically accelerating and reducing the cost of drug development. Rather than relying on Big Pharma’s slow, expensive trial-and-error model—where 90% of new drugs recycle old structures and fail 90% of the time—Metanova crowdsources predictions from miners to discover novel compounds that bind to desired targets (like serotonin or dopamine) while avoiding off-target effects. Their platform has already screened 4.8 million molecules across 7,000 protein targets, a scale nearly impossible in traditional biotech. Though behavioral drugs (targeting reward, mood, and learning) are the team’s initial R&D focus, Metanova is target-agnostic and open to third-party use. Its platform can be monetized at every stage—offering screening as a service, model training, or licensing candidate compounds—with fiat revenue expected in months, flowing back into their native Nova token over time. With wet lab testing imminent and multiple revenue paths (including potential collaborations with pharma), Metanova positions itself as a platform biotech model—not chasing one miracle drug but creating a machine that continuously produces valuable assets. Their decentralized AI approach not only outpaces centralized competitors, but opens the door to truly novel therapies that could improve lives at population scale.
the NOVA combinatorial dataset is now live 🔥
miners are already generating molecules using this dataset, like one of the 100 winners from our last challenge (see below).
this marks the first in silico synthesis ever performed on-chain.
we’re starting with reductive amination…
Fantastic episode of Revenue Search with @micaelabazo and @siamkidd – here’s what I learned about how @metanova_labs is reinventing drug discovery.
Traditional pharma spends $2.6 billion and 10 years trying to develop each new drug. Nearly 90% of that effort fails before…
in case you missed it 👇
our ceo @micaelabazo on #bittensor revenue search with @SiamKidd and @markcreaser
Bittensor Revenue Search 08: Micaela Bazo - SN68 Metanova https://x.com/i/broadcasts/1djGXVorgnBxZ
see you tomorrow 11 am UK time
2 Bittensor Revenue Searches tomorrow (Friday 04 Jul)...
1100 - SN68 @metanova_labs
&
1400 - SN50 @SynthdataCo
See you then!
“we at @metanova_labs launched a subnet with the goal of fast-tracking the development of the most asymmetric real-world assets possible” and to:
- design metaprogramming drugs that shape thought, emotion, behavior, define our health, and increase our sense of agency
- advance…
don’t lose the plot. dtao was meant to eliminate apathy, not your ability to believe in the mission.
it was designed to curb oligarchic voting and stop emissions from bleeding into scammy or stagnant subnets, projects that, over time, could have brought the entire network down.…
NOVA is the most capital-efficient drug screening platform
4.6M molecule submissions
against 7K protein targets
from a 1B+ compound library
in just 4 months
→ the largest decentralized screening ever
without upfront costs in compute and instant access to a cracked pool of…
Keep ahead of the Bittensor exponential development curve…
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