Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −21.11%: protocol buys +10.01%, root sells −1.28%, users net −30.27%, cross-subnet +0.43%, unattributed +0.0002%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-05 UTC, as of ≈23:01 UTC (block 9,442,999)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Drug discovery requires screening billions of potential compounds and designing novel biologics to identify molecules with high binding affinity to disease targets. This screening process is computationally intensive, expensive, and centralizes discovery capability within well-funded organizations, limiting innovation and increasing time-to-candidate timelines.
A network of independent contributors competing to discover optimal molecules and design superior nanobody sequences can parallelize the search across vast chemical and sequence spaces while economic incentives reward participants who consistently identify the highest-affinity binders. This approach distributes both computational burden and discovery risk while maintaining quality through rigorous validation.
NOVA implements this model on Bittensor by aggregating miner submissions of small-molecule identifiers and nanobody sequences, validating them against chemical and biological constraints, scoring them with specialized ML models (Boltz-2 for binding affinity prediction and BoltzGen for nanobody complex scoring), and ranking winners based on predicted binding quality and developability metrics.
Validators run the scoring pipeline and set network weights to reward top performers, creating a competitive marketplace for drug discovery contributions.
Auto-generated from the GitHub repository and may not reflect the latest changes.
NOVA (SN68) is a Bittensor subnet that accelerates drug discovery by harnessing distributed compute to screen large chemical spaces for drug-like molecules and design nanobody sequences with high predicted binding affinity to protein targets. Miners compete by discovering small molecules or designing novel nanobody sequences that bind target proteins strongly; submissions are validated and scored using Boltz-2 for molecules and BoltzGen for nanobodies, with winners determined by ranking across multiple binding quality and developability metrics.
Miner → Commits encrypted submission (pointer) to chain + uploads encrypted payload to GitHub → Validator retrieves commitment from chain, fetches encrypted payload from GitHub, decrypts submission → Validator runs validity checks (chemistry/biology rules) → Validator scores valid submissions with ML models (Boltz-2 for molecules, BoltzGen for nanobodies) → Validator aggregates scores and determines winner per type → Validator sets weights on-chain (73.7% to UID 0, remainder to winner). Score sharing: validators retrieve and average scores across validator runs via external API for robustness with non-deterministic models.
For small molecules: Boltz predicts binding affinity. If multiple molecules submitted, fingerprint entropy is calculated. Scores are averaged across multiple targets (if applicable), then summed across molecules.
Winner is UID with best final_molecule_score (determined by boltz_mode: max or min), with ties broken by earliest block_submitted, then push_time, then UID. For nanobodies: BoltzGen produces scores across 10 metrics (design_iiptm, design_ptm, design_to_target_iptm, min_design_to_target_pae, interaction_pae, plip_hbonds_refolded, plip_saltbridge_refolded, delta_sasa_refolded, liability_score, liability_num_violations). All miners are ranked per-metric independently; final score is sum of ranks (lower is better).
Scores are averaged across targets, then summed across sequences. Winner is UID with best final_nanobody_score using boltzgen_rank_mode (typically min for ranking). Tie-breaking identical to molecules.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
Accelerating drug discovery.
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Not yet verified: Notes
Not yet verified: Notes
Machine-extracted from the subnet's repository docs; may be wrong or out of date. Check the repository before buying or renting hardware. Open the repository (github.com, opens in a new tab, external site) · Extracted 2 Nov 2026
Clone the repository with submodules
git clone --recurse-submodules https://github.com/metanova-labs/nova.git && cd nova
Create and configure .env file with SUBTENSOR_NETWORK, GITHUB_TOKEN, GITHUB_REPO_* settings, and GPU allocation (NOVA_BOLTZ_SHARDS, NOVA_BOLTZ_GPUS, NOVA_BOLTZGEN_GPUS, NOVA_BOLTZ_THREADS, NOVA_MAX_CONCURRENT_TNP)
cp example.env .env && nano .env
Install dependencies (CUDA version optional, defaults to 12.6)
./install_deps.sh [--cuda <version>] && source "$HOME/.local/bin/env"
Activate virtual environment
source .venv/bin/activate
Run the miner
python3 neurons/miner.py --wallet.name <your_wallet> --wallet.hotkey <your_hotkey> --logging.info
Clone the repository with submodules
git clone --recurse-submodules https://github.com/metanova-labs/nova.git && cd nova
Create and configure .env file with SUBTENSOR_NETWORK, GITHUB_TOKEN, VALIDATOR_API_KEY, SCORE_SHARE_API_URL, AUTO_UPDATE, and GPU allocation settings
cp example.env .env && nano .env
Install dependencies with CUDA 12.6
./install_deps_cu126.sh
Activate virtual environment
source .venv/bin/activate
Run validator in normal mode (requires registration and stake)
python3 neurons/validator/validator.py --wallet.name <wallet_name> --wallet.hotkey <hotkey_name> --logging.debug
Run validator in test mode (skips registration and stake checks)(optional)
python3 neurons/validator/validator.py --wallet.name <wallet_name> --wallet.hotkey <hotkey_name> --test_mode --logging.debug
| Day (UTC) | Share |
|---|---|
| 2026-11-04 | 2.18% |
| 2026-11-03 | 2.14% |
| 2026-11-02 | 2.04% |
| 2026-11-01 | 2.00% |
| 2026-10-31 | 1.99% |
| 2026-10-30 | 1.91% |
| 2026-10-29 | 1.95% |
| 2026-10-28 | 1.93% |
| 2026-10-27 | 1.95% |
| 2026-10-26 | 1.95% |
| 2026-10-25 | 1.93% |
| 2026-10-24 | 1.93% |
| 2026-10-23 | 1.98% |
| 2026-10-22 | 1.98% |
| 2026-10-21 | 2.00% |
| 2026-10-20 | 2.05% |
| 2026-10-19 | 2.03% |
| 2026-10-18 | 2.05% |
| 2026-10-17 | 2.15% |
| 2026-10-16 | 2.28% |
| 2026-10-15 | 2.36% |
| 2026-10-14 | 2.42% |
| 2026-10-13 | 2.44% |
| 2026-10-12 | 2.44% |
| 2026-10-11 | 2.45% |
| 2026-10-10 | 2.46% |
| 2026-10-09 | 2.47% |
| 2026-10-08 | 2.48% |
| 2026-10-07 | 2.60% |
| 2026-10-06 | 2.75% |
| 2026-10-05 | 2.73% |
| 2026-10-04 | 2.72% |
| 2026-10-03 | 2.79% |
| 2026-10-02 | 2.79% |
| 2026-10-01 | 2.71% |
| 2026-09-30 | 2.80% |
| 2026-09-29 | 2.68% |
| 2026-09-28 | 2.63% |
| 2026-09-27 | 2.64% |
| 2026-09-26 | 2.79% |
| 2026-09-25 | 2.75% |
| 2026-09-24 | 2.69% |
| 2026-09-23 | 2.71% |
| 2026-09-22 | 2.77% |
| 2026-09-21 | 2.85% |
| 2026-09-20 | 2.87% |
| 2026-09-19 | 2.82% |
| 2026-09-18 | 2.76% |
| 2026-09-17 | 2.77% |
| 2026-09-16 | 2.77% |
| 2026-09-15 | 2.82% |
| 2026-09-14 | 2.88% |
| 2026-09-13 | 2.83% |
| 2026-09-12 | 2.83% |
| 2026-09-11 | 2.79% |
| 2026-09-10 | 2.74% |
| 2026-09-09 | 2.96% |
| 2026-09-08 | 3.39% |
| 2026-09-07 | 3.53% |
| 2026-09-06 | 3.48% |
| 2026-09-05 | 3.51% |
| 2026-09-04 | 3.55% |
| 2026-09-03 | 3.62% |
| 2026-09-02 | 3.38% |
| 2026-09-01 | 2.81% |
| 2026-08-31 | 2.81% |
| 2026-08-30 | 2.83% |
| 2026-08-29 | 2.94% |
| 2026-08-28 | 3.09% |
| 2026-08-27 | 2.89% |
| 2026-08-26 | 2.84% |
| 2026-08-25 | 2.87% |
| 2026-08-24 | 2.93% |
| 2026-08-23 | 2.92% |
| 2026-08-22 | 2.93% |
| 2026-08-21 | 2.94% |
| 2026-08-20 | 2.93% |
| 2026-08-19 | 3.00% |
| 2026-08-18 | 3.09% |
| 2026-08-17 | 3.22% |
| 2026-08-16 | 3.28% |
| 2026-08-15 | 3.20% |
| 2026-08-14 | 3.25% |
| 2026-08-13 | 3.27% |
| 2026-08-12 | 3.34% |
| 2026-08-11 | 3.41% |
| 2026-08-10 | 3.49% |
| 2026-08-09 | 3.39% |
| 2026-08-08 | 3.42% |
| 2026-08-07 | 3.30% |
