Physio-DPO: Aligning Large Language Models with the Protein Energy Landscape to Eliminate Structural Hallucinations

TL;DR

Physio-DPO is a physics-informed alignment method for protein language models that reduces structural hallucinations by incorporating thermodynamic stability, leading to more accurate and foldable protein structures.

Contribution

It introduces a magnitude aware objective that aligns protein models with the energy landscape, improving stability and reducing hallucinations compared to existing methods.

Findings

Reduces RMSD to 1.28 Å

Achieves 92.8% foldability

Mitigates structural hallucinations effectively

Abstract

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 \AA\ 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.