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

Published: June 5, 2025 | arXiv ID: 2506.05596v1

By: Jes Frellsen , Maher M. Kassem , Tone Bengtsen and more

Potential Business Impact:

Improves protein stability predictions for new medicines.

Business Areas:
A/B Testing Data and Analytics

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.

Country of Origin
🇩🇰 Denmark

Page Count
16 pages

Category
Computer Science:
Machine Learning (CS)