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

A Dual- Task Hierarchical Graph Attention Network for Protein-Protein interaction sites Prediction

An innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions and gives a better understanding of the structural processes that control PPI.

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.

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

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.

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

FerrGAT: Multi-Task Pre-Training of Graph Attention Networks for Low-Data Molecular Activity Prediction

Predicting molecular bioactivity in low-data regimes remains a central challenge in computational drug discovery, where labeled compounds for specialized tasks are scarce while related datasets are abundant. Here, we propose FerrGAT, a graph attention network framework that addresses this challenge through domain-relevant multi-task pre-training and dual-channel molecular representation learning. FerrGAT first pre-trains a shared GAT encoder on three mechanistically related tasks—antioxidant activity (GST inhibition, 245 compounds from ChEMBL target CHEMBL2095173; 83 active/162 inactive), kinase inhibition (3000 compounds spanning AXL, EGFR and VEGFR2 from ChEMBL; 2240 active/760 inactive), and cellular toxicity (7265 compounds from the Tox21 NR-AhR endpoint; 309 active/6956 inactive)—then transfers the learned representations to a target task via differential learning rate fine-tuning. The architecture fuses atom-level graph features from multi-head attention message passing with global physicochemical descriptors through a learned projection and provides built-in interpretability via attention weight visualization at the atomic level. We evaluated FerrGAT on ferroptosis inhibitor prediction as a representative low-data molecular classification task (1052 compounds from ChEMBL targets GPX4 and HMOX1 combined with 63 literature- and FerrDb-curated ferroptosis modulators; 409 active/643 inactive). In 5-fold cross-validation, FerrGAT achieved an AUC of 0.906, outperforming Morgan fingerprint baselines, including Random Forest (0.877), XGBoost (0.871), and an SVM (0.873). Ablation studies confirmed that domain-relevant pre-training improved AUC by 2.5% over training from scratch, while pre-training on unrelated tasks degraded performance, highlighting the importance of task-domain alignment. Applied to virtual screening of 30 FDA-approved tyrosine kinase inhibitors, the model identified Bemcentinib (AXL inhibitor, score = 0.918) as a top candidate, validated by AutoDock Vina molecular docking (−8.51 kcal/mol) and independent experimental evidence, including lipid peroxidation assays, Western blot, and cellular thermal shift analysis. These results demonstrate that domain-aware transfer learning with graph attention networks provides an effective and interpretable framework for molecular property prediction in data-limited scenarios.