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Metanova says NOVA has begun nanobody production in partnership with Yalotein, which starts experimental validation for the project's nanobody track.
The update moves NOVA, Bittensor's Subnet 68, beyond purely computational screening for that therapeutic category. According to Metanova's announcement, the subnet now runs two therapeutic modalities through both virtual screening and experimental validation, small molecules and nanobodies.
NOVA's core thesis depends on more than producing high-scoring predictions onchain. For AI-native drug discovery to be useful outside crypto, promising outputs eventually have to be tested in physical laboratory conditions. Yalotein gives NOVA a wet-lab partner to evaluate whether selected nanobody candidates perform beyond the virtual scoring environment.
That step matters because many AI drug-discovery projects never move beyond digital prediction. NOVA is now testing whether competitive, AI-generated nanobody designs can become actual biological molecules under experimental feedback.
What NOVA Is Adding With Nanobodies
NOVA is a decentralized drug-discovery network built on Bittensor. Its miners compete to generate or identify therapeutic candidates, while validators evaluate submissions through the subnet's incentive system. The goal is to turn early-stage drug discovery into a distributed optimization problem, where contributors earn rewards for producing higher-quality candidates.
Antonio Verrico
Until now, much of the public discussion around NOVA has focused on small molecules, the low-molecular-weight compounds that make up a large share of traditional drug discovery. Nanobodies are a different class of candidate. They are small, single-domain antibody fragments that can bind targets with high specificity and are used across therapeutic, diagnostic, and research applications.
Small molecules and nanobodies require different discovery, production, and validation workflows. By expanding into nanobodies, NOVA is testing whether the same Bittensor-based incentive model can support several kinds of therapeutic discovery under one system, not just adding another challenge format.
Metanova framed the update that way, saying NOVA is being built as a discovery engine, not infrastructure for a single target or modality.
Yalotein's Role in Validation
Yalotein connects NOVA's computational discovery process to experimental validation. In an earlier Metanova post about the collaboration, the team said Yalotein would advance 50 nanobody candidates identified by NOVA into in-vitro testing, and that it was preparing to test the top 50 nanobody submissions in the wet lab.
A subnet can rank, score, and reward candidate generation, but drug discovery ultimately depends on real-world evidence. Wet-lab testing helps determine whether computationally promising candidates can actually be produced, bind as expected, and justify further research.
This feedback loop is key for NOVA. If experimental results feed back into future challenges, the subnet can improve how it searches, scores, and prioritizes candidates over time, moving closer to a process where virtual screening and physical validation reinforce each other instead of running as separate stages.
What This Means for Bittensor Drug Discovery
NOVA is trying to show that subnet incentives can coordinate useful scientific work, not just produce digital outputs. Drug discovery is a hard test case because predictions alone are not enough. A candidate's value depends on whether it can survive the next stages of research, including synthesis, binding assays, safety evaluation, and much more extensive preclinical and clinical work.
The Yalotein partnership gives NOVA a clearer path from onchain competition to laboratory evidence, and it strengthens the case for multimodal discovery. If the subnet can support both small molecules and nanobodies, it depends less on any single research track and works as a more general discovery engine for different therapeutic formats.
