[ML Seminar] IDA ML seminar - Jes Frellsen - Why are inverse folding models good zero-shot predictors of protein thermodynamic stability?

[Louis Ohl]

Dear all,

The IDA machine learning seminar is today at 13.30 in Alan Turing.

Best regards,
--
Louis

________________________________
Från: Louis Ohl <louis.ohl at liu.se>
Skickat: den 26 februari 2026 08:00
Till: idaint at ida.liu.se <idaint at ida.liu.se>; ml-seminars at lists.liu.se <ml-seminars at lists.liu.se>
Ämne: IDA ML seminar - Jes Frellsen - Why are inverse folding models good zero-shot predictors of protein thermodynamic stability?


Welcome to an IDA Machine Learning Seminar on Wednesday, March 11 at 13:30 In Alan Turing



Why are inverse folding models good zero-shot predictors of protein thermodynamic stability?



Jes Frellsen<https://frellsen.org/>, Technical University of Denmark, Cognitive Systems



Abstract: Inverse folding models are trained to recover sequences from structures, yet they have emerged as highly effective zero-shot predictors of protein stability. How can we understand this connection? In this talk, I unpack the theoretical assumptions that connect the amino acid preferences of an inverse folding model to the free-energy considerations that govern thermodynamic stability. Drawing on concepts from probability theory and statistical physics, I will show that commonly used heuristics can be interpreted as simplistic approximations and that more principled alternatives empirically yield considerable performance gains.

Bio: Jes Frellsen is an Associate Professor at the Technical University of Denmark (DTU). He received his PhD in Bioinformatics from the University of Copenhagen. Before joining DTU, he was a postdoc in the Machine Learning Group at the University of Cambridge and an Associate Professor at the IT University of Copenhagen. At DTU, he leads a research group on probabilistic machine learning and generative AI. His methodological contributions include work on missing data, uncertainty quantification, and out-of-distribution detection, with applications in bioinformatics, physics, recommender systems, and remote sensing. He has authored more than 70 research articles and book chapters, with numerous at the premier machine learning venues. He contributes actively to the community through conference chairing and as a founding co-organiser of the GeMSS summer school on deep generative models. He was recently awarded the Jorck’s Foundation Research Prize for his contributions to methods for handling incomplete data and enabling the safe and reliable use of AI.



Location: Alan Turing, https://link.mazemap.com/RDyxFvXH



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--
Louis Ohl
Postdoc
louis.ohl at liu.se

Linköping University
IDA / STIMA
B-Huset, 2A:445
Webpage<https://oshillou.github.io/>
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