Aug 2026· Bioinformatics· Vol 42· 0 citations· 38 references
Medicine
TL;DR
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
Abstract
Abstract Motivation The rapid advancement of generative artificial intelligence has enabled the high-throughput design of therapeutic antibody candidates. However, the precise evaluation of these candidates remains a significant challenge due to the scarcity of high-quality activity data and the structural flexibility of antibody complementarity-determining regions (CDRs). Results To address these challenges, we propose AbAgKer, an antibody screening model leveraging pre-trained representations and biological prior guidance for antigen-antibody affinity and kinetics prediction. Specifically, we design a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes. To mitigate data scarcity, we employ a semi-supervised learning strategy for data self-distillation, which significantly enhances affinity prediction performance. Additionally, we demonstrate that the interaction representations learned by AbAgKer can be effectively transferred to the data-scarce task of predicting dissociation rates via few-shot learning. Extensive experiments demonstrate that AbAgKer outperforms baseline models and exhibits strong generalization capabilities in antibody screening and drug residence time analysis. Availability and implementation The source code and dataset are available at https://github.com/CSUBioGroup/AbAgKer and https://doi.org/10.5281/zenodo.19691211.
This work proposes MochiBind, a sequence-only pairwise binding affinity predictor, and benchmark it against structure-derived baselines such as Boltz-2, GeoDock, and Graphinity, suggesting that sequence-based approaches can match or surpass structure-based models in generalization.
Yunrui Li, Yue Zhao, K. Sonmez et al.· iScience· 0 citations
This thesis examines the integration of machine learning into computational structural biology, with an emphasis on modelling and predicting antibody–antigen interactions. Such interactions are fundamental to numerous biological processes and are central to therapeutic antibody design. Despite recent advances in AI-based protein structure prediction, antibodies remain particularly challenging targets due to the high variability of their complementarity-determining regions, the limited availability of experimental structures, and the lack of strong co-evolutionary signal.
To address these challenges, this work introduces several methodological contributions. In Chapter 2,DeepRank-GNN-esm incorporates embeddings from protein language models to replace computationally expensive evolutionary features, thereby improving both predictive performance and efficiency in scoring protein–protein complexes. In Chapter 3, a modelling pipeline is introduced that employs a flow-matching algorithm to effectively sample the conformational diversity of the antibody CDR-H3 loop. When integrated with ensemble docking, this approach significantly improves the accuracy of antibody–antigen complex modelling compared to existing methods. In Chapter 4, the thesis presents AbTune, a sequence-specific fine-tuning strategy for protein language models that enhances predictive performance across multiple antibody-related tasks, including structure prediction, mutation effect estimation, and binding affinity prediction, while remaining computationally efficient. In Chapter 5, DeepRank-Ab is developed as a geometric deep learning-based scoring function tailored to antibody–antigen complexes, achieving state-of-the-art performance in ranking near-native docking conformations. Chapter 6 summarizes the main findings of the thesis and discusses future research directions.
Collectively, these contributions demonstrate how machine learning can be applied to address key limitations in antibody modelling and to facilitate the rational design of antibody-based therapeutics.
The resulting model, HydrAffinity, is an interaction-free, dynamic sparse model that uses pre-trained encoders and MoE for parameter-efficient learning and outperforms all interaction-free methods and matches state-of-the-art interaction-based methods on CASF-2016.
AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context, achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.
Xiaoliang Shi, Zichen Wang, Runze Ma et al.· 0 citations
This review compiles the data resources related to polyreactive antibodies and places a particular emphasis on computational models for predicting antibody polyreactivity, which includes empirical models based on physicochemical properties, traditional machine learning models, deep learning networks, and protein language models.
Haoxian Tang, Zixuan Zhang, Wenzhi Li et al.· Computational Biomedicine· 0 citations
SAASBench provides a framework for evaluating the model's ability to estimate the specificity of a candidate antibody in relevant settings, indicating that strong performance on traditional affinity benchmarks does not automatically translate into reliable antibody specificity estimation in proteome-derived settings.
Dmitriy Umerenkov, Ivan Poddiakov· Proceedings of the 32nd ACM...· 0 citations