enFoldX extracts 106 interface and confidence features from AF3 ensembles
quaidmorris · x · 2026-07-22
enFoldX details its ensemble-based feature pipeline
This reply explains how the model works:
- It folds an ensemble of 10 seeds × 5 samples.
- For each prediction, it extracts 106 structural, confidence, and biophysical features.
- The emphasis is on the TCR:pMHC interface and peptide–CDR3 contacts.
- Each feature is summarized with both the mean and standard deviation across the ensemble.
The result is a classifier that can exploit structure uncertainty rather than collapsing everything into one best guess.
More from Research
- Stanford Team Introduces Gigatoken, the World's Fastest Tokenizer — StanfordAILab · 2026-07-22
- Tabul AI launches Metal TreeSHAP to speed up Shapley values on Apple silicon — Scobleizer · 2026-07-22
- Reddit points to OpenAI’s ChatGPT Ads page — EcstaticAsparagus509 · 2026-07-22
- Open-source runtime lets each repo define its own AI code reviewer — ibabufrik · 2026-07-22
- DeepSWE: A New Benchmark for Evaluating AI Coding Agents on Real GitHub Issues — pmz · 2026-07-22
- A Rust space-economy sim runs hundreds of autonomous ships, built with Claude — kalcode · 2026-07-22