Explainability from Training with Applications to TCR-Epitope Prediction

arXiv:2609.36354 · cs.LG, cs.AI, cs.CE, q-bio.BM · Submitted 2026-09-28 · Read on arXiv

cs.LG, cs.AI, cs.CE, q-bio.BM

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/aqlaboratory/openfold-3

Terminology

• Benchmark — 23× in this paper · explained in 2 episodes
• Transformer — 19× in this paper · explained in 2 episodes
• alpha — 14× in this paper · explained in 1 episode
• Regularization — 11× in this paper · explained in 3 episodes
• Explainability — 10× in this paper · explained in 3 episodes
• Interpretability — 10× in this paper · explained in 15 episodes
• Clustering — 7× in this paper · explained in 2 episodes
• Explainability From Training — 7× in this paper
• AUC — 6× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Model-Agnostic — 4× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Computational Biology — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Weight-sparse transformers have interpretable circuits
• TCR-EML: Explainable Model Layers for TCR-pMHC Prediction
• Structure-Regularized Interpretable TCR-Epitope Prediction

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