A Markov-bridge inverse folding model fine-tuned with energy-based preference pairs and an explicit ΔΔG loss designs lower-energy protein complex sequences while keeping sequence recovery close to state-of-the-art.
Robust deep learning–based protein sequence design using proteinmpnn,
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EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
A Markov-bridge inverse folding model fine-tuned with energy-based preference pairs and an explicit ΔΔG loss designs lower-energy protein complex sequences while keeping sequence recovery close to state-of-the-art.