HyInv Technical Report
- Published
- Source
- arXiv
- Paper number
- 1439
- Field
- Machine Learning
- arXiv ID
- 2611.11997
Key points
- The authors trained HyInv, a 1.96-million-parameter backbone-conditioned graph network, with random-order autoregressive pretraining followed by structure-reward reinforcement learning.
- A sampler that drafts at a higher temperature and rewrites at a lower temperature improved refolding TM-score at matched diversity.
- An optimized implementation with BF16 numerics designs 128 sequences for a 402-residue backbone in 50.6 ms on a single NVIDIA H20 GPU, about 52 times faster than the reference sampler.
- Across five benchmark datasets and seven dataset-predictor combinations, HyInv reaches a higher average scTM than all ten external baselines at comparable or higher sequence diversity.
- The average margin is +0.013 over ProteinMPNN and +0.008 over ProteinZero V2.
- The same default setting supports de novo backbones, pocket-constrained libraries, enzyme redesign with the catalytic site kept, and loop redesign.
Paper links
External research summaries. These are not HDATF publications or measured product results.