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Metanova Labs plans to allocate 26% of NOVA’s emissions to the subnet owner, directing the additional allocation into a dedicated research and development fund for experimental drug-discovery data. The planned split leaves 33% for miners and 41% for validators on Bittensor’s Subnet 68.
The new allocation will fund wet-lab measurements used to train and evaluate models, beginning with an ADME model-training competition that Metanova is designing. The fund may also support experimental work on therapeutic assets already under development.
The decision connects NOVA’s token incentives to a practical research expense, producing laboratory evidence that can improve computational predictions and test whether they hold up experimentally. Metanova argues that better data will make both miner submissions and the subnet’s evaluation process more useful, even with a smaller miner allocation than Bittensor’s legacy standard.
How the Emission Allocation Changes
Bittensor’s Etao upgrade replaced the network’s fixed participant split with owner-configurable allocations. NOVA is using those controls to reserve a larger share for laboratory work rather than distributing that share directly to miners.
Compared with the legacy standard, the planned allocation moves eight percentage points from the miner category to the owner category:
| Recipient | Legacy Standard | NOVA’s Planned Split |
|---|---|---|
| Owner | 18% | 26% |
| Miners | 41% | 33% |
| Validators | 41% | 41% |
Under the Etao protocol rules, owners can receive up to 33%, miners must receive at least 33%, and validators can receive no more than 50%. The validator allocation must also exceed root stakers’ live baseline share when a change is made. NOVA’s planned miner share is at the protocol minimum, while its owner share remains below the cap.
Laboratory Data for ADME Models
ADME stands for absorption, distribution, metabolism, and excretion, the processes that determine how a compound enters the body, reaches different tissues, is broken down, and is cleared. These properties help researchers assess whether a promising molecule has characteristics suitable for further drug development, beyond its ability to bind a target.
Metanova’s first planned use of the fund is to generate standardized experimental data for models predicting those properties. The ADME competition is in design and development, with the laboratory work intended to provide a stronger basis for training and assessing submissions.
The company identifies fragmented public datasets as a constraint. Results collected across different studies can reflect different protocols, quality standards, and areas of chemical coverage. Metanova wants to generate measurements that give miners more reliable ground truth and validators an independent experimental reference for judging predictions.
That approach fits NOVA’s existing discovery workflow, which combines open competitions, shortlisted submissions, wet-lab validation, and the integration of experimental results into subsequent model development. Laboratory measurements can serve two roles in that cycle, checking the usefulness of a prediction and supplying evidence for the next round of computational work.
The planned ADME work also complements Metanova’s hit-to-lead optimization agent with Chutes and Onepot AI. That system is being developed to help researchers refine promising small molecules across competing requirements, including binding affinity, selectivity, and physicochemical properties. Experimental activity testing and ADME validation follow that optimization work.
