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.

By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis and achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation.

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

MIRAGE provides an interpretable and robust framework for structure-aware prediction, with potential applications in protein engineering and drug design, and explicitly modeling multi-level interactions is important for accurately capturing the determinants of binding affinity.

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.