Structure-Agnostic Protein-Ligand Binding Affinity Prediction via Hierarchical Representation Alignment.

DiConSite: A Unified Topology-Adaptive Architecture for Protein Binding Site Prediction Across Ligand Modalities.

Accurate identification of protein binding sites is essential for understanding biological mechanisms and advancing drug design. However, many structure-based predictors rely on spatial graphs whose topology remains fixed throughout message passing, making them sensitive to structural noise and difficult to transfer across ligand modalities. To address this issue, we propose DiConSite, a topology-adaptive and reusable architecture for residue-level binding site prediction across ligand-specific tasks. DiConSite is centered on a Latent Topological Evolution (LTE) module that augments the initial Euclidean graph with a latent functional topology. A Hierarchical Topological Distillation (HTD) objective and a Dynamic Curriculum Distillation (DCD) schedule are further introduced as LTE-dependent optimization stabilizers: they align relational structure across network depths only after the underlying topology has been refined. Extensive experiments across nine benchmarks show that DiConSite achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation. By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis.

Hierarchical Graph Representation Learning From a Statistical Perspective for Generalizable and Interpretable Protein-Ligand Binding Affinity Prediction.

Protein-ligand binding affinity (PLA) prediction aims to guide rational drug design by estimating the strength of interaction. The effectiveness of the representation learning of protein and ligand is key to successful PLA prediction. To this end, attention mechanism, as a powerful architectural paradigm, has been introduced and gradually emerged as the prevailing approach. However, intuitively, the classical attention paradigm based on similarity does not fit the biological mechanisms relevant for binding. Worse still, the cooperative and antagonistic effects among multiple atoms are deliberately disregarded in the classical formulation of attention mechanisms. Consequently, the rigid transplantation of classical architectures substantially undermines the PLA prediction performance. To address these challenges, we employ a hierarchical statistical attention model (HISA). Specifically, HISA employs a statistical attention mechanism (SAM) based on non-similarity computation to fit the biological prior and perceive the relationship of multiple atoms. In addition, we optimize HISA by employing clustering, enabling hierarchical representations of biomolecules. Extensive experiments demonstrate that HISA achieves state-of-the-art performance on multiple PLA benchmarks while simultaneously exhibiting generalizability and interpretability.

A Preparation-Free Mixture-of-Experts Framework for Protein-Ligand Affinity Prediction

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.

MSCA-PLA: multi-scale cross-attention with differentiable pooling for protein-ligand binding affinity prediction.

Interpretable multilevel interaction modeling for robust protein–protein affinity

Quantifying protein–protein binding affinity is essential for understanding molecular recognition and guiding antibody and inhibitor design. However, binding affinity is governed by tightly coupled sequence, structural, and chemical determinants. Existing models often encode these factors in isolation, limiting their ability to capture the multi-level dependencies underlying binding affinity  $$\left(\Delta\text{G}\right)$$ . We propose MIRAGE, a graph-based framework for direct $$\Delta \text{G}$$ prediction that explicitly models interactions across multidimensional (1D sequences, 2D contact maps, 3D structures) and multi-scale (residue-level, atom-level) features. MIRAGE integrates two complementary modules to capture cross-dimensional and cross-scale dependencies, enabling unified residue–atom representation learning. Across public benchmarks, MIRAGE demonstrated strong generalization, achieving Pearson correlations of 0.70 and 0.69 on two independent external test sets and retaining predictive effectiveness under structure-separated cross-validation designed to reduce structural information sharing. In a supplementary analysis with AlphaFold3-predicted complex structures, MIRAGE also preserved significant predictive correlations when experimentally resolved structures were unavailable. Ablation studies confirm the contributions of each module. Interpretability analyses further show that the model focuses on biophysically meaningful interface regions. The source code of MIRAGE is available from https://github.com/ShiweiWu-545/MIRAGE . These results indicate that explicitly modeling multi-level interactions is important for accurately capturing the determinants of binding affinity. MIRAGE provides an interpretable and robust framework for structure-aware $$\Delta \text{G}$$ prediction, with potential applications in protein engineering and drug design.

AbAgKer: A Unified Semi-Supervised Framework for Antigen-Antibody Binding Affinity and Kinetics Prediction.

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.