Turning Noise into Signal: Predicting TCR Binding Using AlphaFold3 Hallucinations
quaidmorris · x · 2026-07-22
A major challenge in computational immunology is predicting the binding of T-cell receptors (TCRs) to peptides presented on MHC molecules (pMHCs). A research team from MSK Cancer Center has introduced a new method called enFoldX.
The researchers discovered that a lot of the signal is actually hiding in AlphaFold3's structural "hallucinations." Instead of relying on a single predicted structure, enFoldX uses the entire noisy ensemble to successfully predict TCR:pMHC binding. The preprint and public code are now available.
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