# Team Negaverse

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Bailin Zhang](https://cerebralvalley.ai/u/lucyzhang1116), [Igor Gerasimov](https://cerebralvalley.ai/u/geravant)
- **GitHub:** https://github.com/Geravant/negaverse
- **Demo video:** https://youtu.be/4RHxGCo-5es
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/79

Negaverse is a Python toolkit for generating biologically informed negative datasets for protein–protein interaction models. The same approach can later extend to protein–ligand, protein–RNA, and protein–DNA tasks.

Most datasets create negatives by randomly pairing proteins. The problem is that some of these pairs may be real but undiscovered interactions, which introduces false labels and teaches models the wrong biology.

Negaverse reduces that risk through a staged pipeline. It first removes known interactions using BioGRID, IntAct, and high-confidence STRING data. It then scores the remaining pairs using network topology and biological and biophysical rules. Only the most uncertain cases are sent to an LLM for literature-based reasoning. Each result includes confidence, difficulty, reasoning, and full provenance.

To test the system, we hid 1,000 real interactions inside the candidate pool. Naive hard-negative mining selected roughly three out of four of them, while Negaverse’s default configuration selected almost none. Across HuRI and DRYAD, Negaverse reduced hidden false negatives by about 10× compared with random sampling.

Every run also produces an interactive report explaining why each pair was selected. A D-SCRIPT model trained on Negaverse negatives already generalizes better to unseen proteins than one trained on random negatives.

The result is more credible and more challenging training data for drug discovery, with fewer false negatives and less wasted experimental validation.

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