Protein Stability Prediction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Zero-shot protein stability prediction by inverse folding models: a free energy interpretation
  5 Jun 2025 1 repository listed
  Inverse folding models have proven to be highly effective zero-shot predictors of protein stability.
• Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy
  25 Oct 2024 1 repository listed
  Accurate prediction of protein mutation effects is of great importance in protein engineering and design.
• Predicting a Protein's Stability under a Million Mutations
  19 Oct 2023 1 repository listed Syntology ran 12 of 15 samples · 3 unverified · 15 pointer-only (licence)
  We build Mutate Everything on top of ESM2 and AlphaFold, neither of which were trained to predict thermodynamic stability.
• Mega-scale experimental analysis of protein folding stability in biology and design
  19 Jul 2023 1 repository listed
  Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900, 000 protein domains in a one-week experiment.
• mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
  8 Feb 2018 1 repository listed
  We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.