Physio-DPO: Aligning Large Language Models with the Protein Energy Landscape to Eliminate Structural Hallucinations
This addresses the issue of unreliable protein design for researchers and practitioners in computational biology by providing a method to align models with thermodynamic stability, though it is incremental as it builds on existing alignment approaches like DPO.
The paper tackled the problem of structural hallucinations in large protein language models, which generate sequences that are linguistically likely but thermodynamically unstable, by proposing Physio-DPO, a physics-informed alignment framework that reduces self-consistency RMSD to 1.28 Å and increases foldability to 92.8%.
Large Protein Language Models have shown strong potential for generative protein design, yet they frequently produce structural hallucinations, generating sequences with high linguistic likelihood that fold into thermodynamically unstable conformations. Existing alignment approaches such as Direct Preference Optimization are limited in this setting, as they model preferences as binary labels and ignore the continuous structure of the physical energy landscape. We propose Physio-DPO, a physics informed alignment framework that grounds protein language models in thermodynamic stability. Physio-DPO introduces a magnitude aware objective that scales optimization updates according to the energy gap between native structures and physics perturbed hard negatives. Experiments show that Physio-DPO consistently outperforms strong baselines including SFT, PPO, and standard DPO, reducing self consistency RMSD to 1.28 Å and increasing foldability to 92.8%. Qualitative analysis further demonstrates that Physio-DPO effectively mitigates structural hallucinations by recovering biophysical interactions such as hydrophobic core packing and hydrogen bond networks.