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A single, frozen protein language model decoder can predict mutation effects across diverse protein families, unlocking generalizable stability landscapes without protein-specific fine-tuning.
Encoding conformational ensembles into a single thermodynamically informed embedding can dramatically enhance cyclic-peptide property prediction accuracy.