Subnet 68
Nova
Metanova Labs
Decentralized ml-powered drug discovery platform incentivizing ai and heuristic innovation to uncover novel therapeutics from massive chemical databases.
SN68 : Nova
| Subnet | Description | Category | Company |
|---|---|---|---|
| SN68 : Nova | Drug discovery | Desci | Metanova Labs |
Drug discovery is traditionally slow, expensive, and risky. Conventional pipelines can take over a decade and billions of dollars to bring a single drug to market [4]. Challenges include:
- Inefficiencies: Incremental chemical modifications rather than breakthrough innovations are common.
- Centralization: Legacy systems suffer from siloed data and limited computational scalability.
- High Cost and Risk: High failure rates add enormous uncertainty and expense.
NOVA addresses these challenges by leveraging the decentralized power of the Bittensor network [5]. By incentivizing the use of ML-based active learning and heuristic adaptive search methods, NOVA transforms drug discovery into a scalable, efficient search process. Every participant—miner or validator—plays a role in rapidly screening a billion-sized molecular library, optimizing the search for high-affinity, synthesizable drug candidates.
NOVA Subnet Architecture
Overview in the Bittensor Ecosystem
NOVA is one of several specialized subnets within the Bittensor network. While other subnets focus on tasks such as pretraining (SN9), data collection (SN13), and protein folding (SN25), NOVA is dedicated to early-stage drug discovery.
Objective: Find the best molecule in the shortest amount of time. NOVA V1 challenges miners to find a molecule from the database provided with the highest binding affinity to the protein target selected for each challenge. Over time, the challenge will evolve into a multi-parametric optimization exercise that mirrors the process of drug discovery with multiple targets and key physicochemical properties that maximize the likelihood of eventual drug approval.
Its architecture comprises three major components:
- Miners: Deploy and execute optimized search methods over the vast chemical space of the SAVI 2020 database hunting promising molecules using for example ML-based active learning and/or heuristic adaptive search methods. Recent approaches such as PyrMD: Accelerated Chemical Space Exploration Using Active Sampling [8] and MolPAL: An Active Learning Framework for Molecular Property Prediction [10] illustrate how active sampling and iterative refinement can efficiently guide exploration.
- Validators: Nodes that evaluate submitted molecules using the Deterministic Oracle.
- The Deterministic Oracle (PSICHIC): A state-of-the-art model that assigns a binding affinity score to each molecule, serving as the “ground truth” for the competition.
Key Interactions
Reward Allocation:
At the end of each challenge, the miner that has presented the molecule with the highest score (binding affinity to the protein) will receive the reward. (See Appendix C for more on the reward allocation rationale).
Mining Process:
Miners extract candidate molecules from SAVI 2020. They refine their strategies over time by using adaptive search techniques (e.g., substructure searches, heuristic filters) or, when applicable, ML-based active learning methods that are adjusted based on the Deterministic Oracle predictions [2, 3]. (See Appendix A for Miners Concept Logic).
Validation Process:
Validators score the submissions for block n at the end of block n+1 using PSICHIC, ensuring objective and transparent evaluation. (See Appendix B for Miners Concept Logic).