vaccines and immunoinformatics approaches

Latest papers

• Explainability from Training with Applications to TCR-Epitope Prediction

  Jiarui Li, Zixiang Yin, Samuel Landry, Zhengming Ding, Ramgopal Mettu · 30 September 2026

  Deep learning models have achieved strong performance in artificial intelligence for science, yet their black-box nature limits our understanding of how they learn scientific tasks. Existing methods for interpretability provide limited insight into how models organize evidence and evolve during lear…

• CaliPPer: quantifying, predicting and improving AI model performance for binding prediction

  Jian-Qing Zheng, Hantao Lou, Zinan Yin, Sam Farrar, Yuze Zhou, Elie Antoun, Xiangxi Wang, Xuetao Cao, Tao Dong · 25 September 2026

  Binding prediction models accelerate therapeutic antibody and TCR discovery, but their performance on new datasets is unpredictable, often leading to low discovery rates. Density-ratio methods (PAPE, M-CBPE) provide label-free performance estimation for binary classification, but their assumptions a…

• Structured Affinity for Unsupervised Visual Class-Incremental Memory in Deep Artificial Immune Networks

  Siphesihle Sithungu · 21 August 2026

  Artificial immune networks (AINs) are naturally memory-forming systems, but conventional visual AINs often rely on flattened vector affinity that ignores spatial structure. This paper studies whether structured, gradient-free immune affinity can make Deep AINs viable as replay-free visual class-incr…

• Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

  Zhongwei Yu, Yan Song, Xue Yan, Anjie Liu, Xingyu Lu, Yihang Chen, Huichi Zhou, Siyuan Guo, Luoyang Sun, Sihan Chen, Xiangning Yu, Jun Wang · 18 August 2026

  Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models such as large language models (LLMs) provide expressive priors over such spaces, but…

• EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?

  Zirui Wang, Jiaqi Wang, Qinghan Wang, Yuzhi Xu, Gang Du, Tingjun Hou, Odin Zhang · 7 August 2026

  Epitopes determine where antibodies bind antigens and shape downstream therapeutic properties such as functional blockade and escape resistance, making epitope understanding central to antibody drug discovery. Although large language models (LLMs) have shown strong biomedical reasoning ability, it r…

• TransNRank: Towards Accurate Neoantigen Ranking with Transformer

  Zhiyin An, Yuenan Hou, Shumeng Duan, Yiming Zhou, Yuanting Zheng, Leming Shi · 4 August 2026

  Personalized neoantigen prediction is challenging due to the scarcity of positive samples, the noise of the experimental data, the severe class imbalance trait and the complex of immunogenicity features. Prior arts, such as linear regression and XGBoost fail to model long-range dependencies and cont…

• Inter-Residue Geometry Attention for Antibody-Specific Epitope Prediction

  Chuanliu Fan, Nan Yu, Junjie Wu, Guohong Fu · 4 August 2026

  Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through a…

• An Early Warning of Emerging Biosecurity Risks in Frontier LLMs

  Zhida He, Xia Hu, Baichen Le, Chunxiao Li, Jiajia Li, Lijun Li, Chaochao Lu, Jing Shao, Youbang Sun, Hua Tang, Xiang Wang, Xiao Wang, Xiaoyu Wen, Tong Wu, Jia Xu, Peng Yu, Shu Yu, Jie Zhang, Qiaosheng Zhang, Yi Zhang, Xing-Ming Zhao, Tianhang Zheng, Ziyuan Zhou · 21 July 2026

  Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace current safeguards. To assess the biological risks of frontier models, we develop Intern-BioBreaker, a specialized bio-red-teaming model, together with a…

• DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers

  Rong Fu, Yongtai Liu, Xiaowen Ma, Haoyu Zhao, Shuo Yin, Yiqing Lyu, Long Zhang, Wangyu Wu · 21 July 2026

  Longitudinal T cell receptor repertoires contain signals of clonal expansion, contraction, disappearance, and reappearance after immune perturbation. Static repertoire language models usually summarize a sample as a bag of sequences, so the sampling interval, sequencing depth, and clone presence pat…

• Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision

  Josh Qixuan Sun, Morteza Babaie, Wenyang Hou, Mark Crowley, David Young · 21 July 2026

  Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data. To address this, we propose a unified preference-based learning framework that integrates scarce quantitative expression data with large-scale weak …

• Structure-Regularized Interpretable TCR-Epitope Prediction

  Jiarui Li, Zixiang Yin, Yunbei Zhang, Janet Wang, Samuel J. Landry, Zhengming Ding, Ramgopal R. Mettu · 1 July 2026

  T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies. Existing sequence- and structure-based models often generalize poorly to unseen epitopes and provide limited interpretability. Furthermore, the impact of generated struct…

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

  Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge, Brook Byrns, Heather L. Wilson, Sureesh Tikoo, Steven Rayan, Gordon Broderick · 30 June 2026

  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. In this work, we evaluate machine learning (ML) approac…

• Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction

  Fang Wu, Weihao Xuan, Jure Leskovec, Yejin Choi, Li Erran Li · 24 June 2026

  Molecular surfaces encode the geometric and physicochemical patterns that determine antibody-antigen recognition, central to epitope prediction. However, existing methods rely on sequences or backbone structures and struggle to capture discontinuous, surface-driven epitopes. This study presents Surf…

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

  Mansoor Ahmed, Huirong Chai, Haoxin Wang, Hemanth Venkateswara, Murray Patterson · 4 June 2026

  Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes. Computational epitope prediction is critical for understanding immune recognition and guiding antibody engineering. However, existing methods face three fundamental challenges: antibody-aware models encode…

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

  Yiming Liao, Yiheng Li, Ning Jiang, Bo Li, Keke Chen · 4 June 2026

  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 d…

• AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design

  Sahil Rahman, Maxx Richard Rahman · 2 June 2026

  Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical feedback or redirect generation when a candidate violates thermodynamic or structural constraints. We introduce AgentPLM, which addresses this by equ…

• Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference

  Sanjar Khudoyberdiev, Arman Bekov · 16 April 2026

  Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning: they exploit spurious correlations in training data -- such as peptide length bias or V-gene co-occurrence -- rather than the physical binding interface. This renders predictions brittle under family-held-out and dis…

• AbLWR:A Context-Aware Listwise Ranking Framework for Antibody-Antigen Binding Affinity Prediction via Positive-Unlabeled Learning

  Fan Xu, Zhi-an Huang, Haohuai He, Yidong Song, Wei Liu, Dongxu Zhang, Yao Hu, Kay Chen Tan · 14 April 2026

  Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design, yet remains constrained by severe label sparsity and the complexity of antigenic variations. In this paper, we propose AbLWR (Antibody-antigen binding affinity List-Wise Ranking), a novel framework that re…

• BioCOMPASS: Integrating Biomarkers into Transformer-Based Immunotherapy Response Prediction

  Sayed Hashim, Frank Soboczenski, Paul Cairns · 2 April 2026

  Datasets used in immunotherapy response prediction are typically small in size, as well as diverse in cancer type, drug administered, and sequencer used. Models often drop in performance when tested on patient cohorts that are not included in the training process. Recent work has shown that transfor…

• ImmSET: Sequence-Based Predictor of TCR-pMHC Specificity at Scale

  Marco Garcia Noceda, Matthew T Noakes, Andrew FigPope, Daniel E Mattox, Bryan Howie, Harlan Robins · 31 March 2026

  T cells are a critical component of the adaptive immune system, playing a role in infectious disease, autoimmunity, and cancer. T cell function is mediated by the T cell receptor (TCR) protein, a highly diverse receptor targeting specific peptides presented by the major histocompatibility complex (p…

• TCR-EML: Explainable Model Layers for TCR-pMHC Prediction

  Jiarui Li, Zixiang Yin, Zhengming Ding, Samuel J. Landry, Ramgopal R. Mettu · 9 March 2026

  T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effectiv…

• AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2

  Faisal Bin Ashraf, Animesh Ray, Stefano Lonardi · 6 March 2026

  Machine learning-based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID-19). The ability of…

• SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

  Rong Fu, Zijian Zhang, Wenxin Zhang, Kun Liu, Jiekai Wu, Xianda Li, Simon Fong · 20 February 2026

  Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority clonotypes. We introduce SubQuad, an end-to-end pipeline that a…

• AntigenLM: Structure-Aware DNA Language Modeling for Influenza

  Yue Pei, Xuebin Chi, Yu Kang · 11 February 2026

  Language models have advanced sequence analysis, yet DNA foundation models often lag behind task-specific methods for unclear reasons. We present AntigenLM, a generative DNA language model pretrained on influenza genomes with intact, aligned functional units. This structure-aware pretraining enables…

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

  Rong Fu, Wenxin Zhang, Muge Qi, Yang Li, Yabin Jin, Jiekai Wu, Jiaxuan Lu, Chunlei Meng, Youjin Wang, Zeli Su, Juntao Gao, Li Bao, Qi Zhao, Wei Luo, Simon Fong · 3 February 2026

  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. We introduce a framework…