Hugging Face Trending Papers June 3, 2026

New Benchmarking Shows Limited Generalization Power of TCR Antigenic Epitope Prediction Models

Read the original on Hugging Face Trending Papers →

Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications. A major limitation is the absence of rigorously defined, unseen benchmark datasets that allow unbiased evaluation of model performance and generalizability.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

benchmarks

arXiv Machine Learning

Jun 4

New Benchmarking Shows Limited Generalization Power of TCR Antigenic Epitope Prediction Models

arXiv:2606. 04994v1 Announce Type: new Abstract: Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications.

By Yiming Liao, Yiheng Li, Ning Jiang, Bo Li, Keke Chen

benchmarks

arXiv Machine Learning

Jul 1

Structure-Regularized Interpretable TCR-Epitope Prediction

arXiv:2606. 30902v1 Announce Type: cross Abstract: T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies.

By Jiarui Li, Zixiang Yin, Yunbei Zhang, Janet Wang, Samuel J. Landry, Zhengming Ding, Ramgopal R. Mettu

llms rag benchmarks safety

arXiv Machine Learning

Jun 30

Transformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS

arXiv:2606. 28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design.

By Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge, Brook Byrns, Heather L. Wilson, Sureesh Tikoo, Steven Rayan, Gordon Broderick

llms

arXiv AI

Jun 9

CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design

arXiv:2603. 13431v3 Announce Type: replace-cross Abstract: Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development.

By Mansoor Ahmed, Nadeem Taj, Imdad Ullah Khan, Hemanth Venkateswara, Murray Patterson

benchmarks

arXiv Machine Learning

Jul 23

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

arXiv:2602. 01051v5 Announce Type: replace Abstract: Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deployment is impeded by label sparsity, cohort heterogeneity, and the computational burden of adapting large encoders to new tasks.

By Rong Fu, Muge Qi, Yang Li, Yabin Jin, Jiekai Wu, Chunlei Meng, Juntao Gao, Li Bao, Qi Zhao, Wei Luo, Youjin Wang, Simon Fong

rag fine-tuning efficiency safety

arXiv Machine Learning

Jun 4

EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning

arXiv:2606. 04154v1 Announce Type: cross Abstract: Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes.

By Mansoor Ahmed, Huirong Chai, Haoxin Wang, Hemanth Venkateswara, Murray Patterson

efficiency benchmarks

Related stories

New Benchmarking Shows Limited Generalization Power of TCR Antigenic Epitope Prediction Models

Structure-Regularized Interpretable TCR-Epitope Prediction

Transformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS

CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning