# Zero-shot protein stability prediction by inverse folding models: a free energy interpretation

arXiv: https://arxiv.org/abs/2506.05596 · HTML: https://mlsanity.com/paper/2506.05596/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 50 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions](https://mlsanity.com/paper/2410.09543/) (2024)

## Built on by (most cited first, 2 of 2)

- [Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators](https://mlsanity.com/paper/2602.11216/) (2026)
- [Deep Learning for Protein Complex Prediction and Design](https://mlsanity.com/paper/2605.11189/) (2026)

## Abstract (arXiv)

Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse folding model and the free-energy considerations underlying thermodynamic stability remains incompletely understood. A better understanding would be of interest not only from a theoretical perspective, but also potentially provide the basis for stronger zero-shot stability prediction. In this paper, we take steps to clarify the free-energy foundations of inverse folding models. Our derivation reveals the standard practice of likelihood ratios as a simplistic approximation and suggests several paths towards better estimates of the relative stability. We empirically assess these approaches and demonstrate that considerable gains in zero-shot performance can be achieved with fairly simple means.