RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
Allies: Prompting large language model with beam search.arXiv preprint arXiv:2305.14766
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RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.