RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
We introduce RosettaSearch, an inference-time multi-objective optimization approach for backbone conditioned protein sequence design. We use large language models (LLMs) as a generative optimizer within a search algorithm capable of controlled exploration and exploitation, using rewards computed from RosettaFold3, a structure prediction model, under a strict computational budget. In a large-scale evaluation, we apply RosettaSearch to 400 suboptimal sequences generated by LigandMPNN (a state-of-the-art model trained for protein sequence design), recovering high-fidelity designs that LigandMPNN's single-pass decoding fails to produce. RosettaSearch's designs show improvements in structural ...
| Authors | Meghana Kshirsagar, Allen Nie, Ching-An Cheng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 30 April 2026 |
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