A function-aware preference alignment framework that improves functional preservation by fine-tuning models to favor function-preserving sequences over function-disrupting alternatives, avoiding the need for explicit function optimization.
Nilufer Tamatgar, Soobin Park, Ying-Hua Yao et al.· Proceedings of the 32nd ACM...· 0 citations
Protein inverse folding models conditioned on structure achieve high sequence recovery but often fail to preserve biological function due to the lack of functional supervision. We propose a function-aware preference alignment framework that improves functional preservation by fine-tuning models to favor function-preserving sequences over function-disrupting alternatives, avoiding the need for explicit function optimization. Our approach constructs reliable preference pairs in silico using hypothesis-driven perturbations of critical residues and model-consistent likelihood constraints, enabling scalable supervision without additional wet-lab measurements. The resulting framework guides protein sequence design models toward generating sequences that better preserve functional integrity, while remaining compatible with existing inverse folding pipelines such as ProteinMPNN and ESM-IF. Extensive experiments on protein design benchmarks and enzyme datasets with established wet-lab validation show that our fine-tuned models consistently outperform pretrained counterparts in preserving functional integrity during protein sequence design. The code is available at https://github.com/EvaFlower/Function-aware-Protein-Inverse-Folding
Nilufer Tamatgar, Soobin Park, Yinghua Yao et al.· Proceedings of the 32nd ACM...· 0 citations
Experiments show that CASE achieves a 37\% average per-setting relative improvement in overall CoT faithfulness over the strongest baselines, exhibits stronger cross-dataset faithfulness transfer, and maintains competitive average accuracy.
This work forms the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and proposes PETA, a parameter-efficient framework that directly adapts pretrained model at test time and outperforms both pretrained and fully retrained baselines while updating only the LayerNorm parameters.
Jia-Qi Lin, Ying-Hua Yao, Chang-Ran Wang et al.· 0 citations
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