Skip to content
Log in

Similar papers

Open access Aug 2026

Structure-agnostic protein–ligand binding affinity prediction via hierarchical representation alignment

Abstract Motivation To enable real-world protein-ligand affinity prediction, not only out-of-distribution generalization but also robustness to variable structural availability and quality should be considered in model design. Results We present AlignNet, a hierarchical representation alignment framework that mitigates intra- and inter-molecular heterogeneity to learn robust protein-ligand embeddings for generalizable affinity prediction, even from sequence-level inputs. Its intra-molecular module projects unimodal and multimodal features into a unified space, aligning augmented multimodal views for feature fusion and unimodal with multimodal embeddings to distill multimodal priors for structure-agnostic inference. Its inter-molecular module aligns protein and ligand embeddings for cross-molecular integration. Extensive experiments show that AlignNet (i) achieves highly competitive performance, with up to a 20.4% gain in SCC on the challenging LBA 30% split under sequence-only settings, suggesting improved out-of-distribution generalization; and (ii) learns well-separated affinity-related clusters, supporting reliable structure-independent prediction. Availability and implementation AlignNet is available at https://github.com/altriavin/AlignNet.

Xiaowen Hu, Hongyi Huang, Hao Sun et al. · 0 citations
Aug 2026

MSIGR-PLA: Integrating Multi-Scale Interaction and Global Representations for Protein-Ligand Affinity Prediction.

The proposed MSIGR-PLA is an integrative framework that integrates local multi-scale interaction features with global protein-ligand representations to improve the accuracy of PLA prediction and consistently outperforms existing methods on four benchmark datasets.

Hangchen Zhang, Haoran Chen, Chang Liu et al. · 0 citations
Open access Aug 2026

GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein–Ligand Binding Affinity Prediction

Comprehensive evaluations indicate that GAMT-GINE can effectively utilize continuous spatial information and heterogeneous affinity labels, achieving good predictive accuracy and cross-dataset generalization capability.

Jiarui Li, Hongquan Li, Di Wu et al. · 0 citations
Open access Jul 2026

Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction

DCAPPI (Dual Cross-Attention network for Protein-Protein Interaction prediction), a novel framework leveraging dual cross-attention modules for hierarchical feature fusion at both intra- and inter-protein levels, achieves superior performance over state-of-the-art methods on benchmark datasets.

Shuai Lu, Yuguang Li, Zhen Tian et al. · 0 citations
Jul 2026

Meta-learning GNN with MD-informed attention for cross-species prediction of phosphoinositide-dependent kinase-1 (PdK1) inhibitors in termite control

This work bridges the gap between data-driven and physics-based approaches, providing a scalable solution for pesticide discovery when target-specific data are limited, and incorporating meta-learning and MD insights improves cross-species transferability and yields interpretable attention patterns based on biophysical principles.

Haroon · 0 citations
Jul 2026

DeepGCL: Multi-View Graph Contrastive Learning for Enhanced Drug-Target Binding Affinity Prediction Through Protein Pocket-Drug Interaction Modeling.

DeepGCL is presented, a novel multi-modal framework that leverages multi-view graph contrastive learning to capture latent representations of pocket-drug interactions and their underlying molecular determinants and underscores the effectiveness of multi-view learning paradigms in capturing the multifaceted nature of drug-target interactions.

Hongmei Wang, Shisen Sun, Mujin Li et al. · 0 citations