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
Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator t...
Andrea Zerio, Yi-Song Yao, Alessandro Micheli et al.· 0 citations
A new method named Reliable Neural Collapse approximation (ReNC) for Open-World Test-Time Adaptation (OWTTA) is introduced, leveraging neural collapse as a structural prior for reliable target-domain adaptation.
Jia-Qi Lin, Yuangang Pan, Chang-Ran Wang et al.· 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-preser...
Nilufer Tamatgar, Soobin Park, Yinghua Yao et al.· Proceedings of the 32nd ACM...· 0 citations
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 La...
Jia-Qi Lin, Ying-Hua Yao, Chang-Ran Wang et al.· 0 citations
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