EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

An algebraic graph neural network model for protein-ligand binding affinity prediction

An Algebraic Graph Neural Network model designed to encode molecular structures into a low-dimensional graph representation while preserving critical biochemical interactions is introduced, demonstrating superior performance in binding affinity prediction compared to state-of-the-art scoring functions.

UniPocket: Physics-Aware Geometric Graph Learning with Manifold Completeness for Ligand-Specific Binding Site Prediction

Native Contact Ratio as a Topological Metric for Machine Learning Based Molecular Docking

Accurate identification of near-native ligand binding poses is a central challenge in structure-based drug design. From a physical point of view, the successful construction of a protein–ligand complex structure is dependent on whether protein and ligand can form enough atomically pairwise interactions that result in a global energy minimum. In this work, we report a machine learning scoring strategy for protein–ligand screening which explicitly considers the Native Contact Ratio (NCR), a topology inspired metric that quantifies the preservation of protein–ligand interfacial contacts as well as interaction energy. This physics-awared supervision strategy provides a simple but efficient gradient field that faithfully reflects the complicated protein energy landscape than conventional 3D coordinate-based objectives. Building on this principle, we present DeepNCR, an energy-informed Transformer framework that encodes approximate Coulombic and dispersive interaction potentials across the protein–ligand binding interface. Furthermore, we introduce a feature pruning step that compresses the interaction tensor from 1470 to 868 dimensions, further improving signal-to-noise ratio and directing model attention toward the interaction motifs critical for binding specificity. The model optimizes topological objectives and at inference drives pose refinement through a differentiable hybrid gradient field integrating predicted NCR and AutoDock Vina energetics. Extensive evaluation on the CASF-2016 benchmark and the 3D-DISCO cross-docking data set demonstrates consistently high performance: a Top-1 docking success rate of 94.7%, a 1% Enrichment Factor of 21.21 in virtual screening, and a Top-1 cross-docking success rate of 34.8%. Mechanistic analysis reveals that NCR-guided optimization enables decoy escaping from local energy minima and drives the recovery of disrupted native interactions, confirming that NCR captures the physical determinants of binding rather than mere geometric proximity.

Application of vision transformers to protein-ligand affinity prediction

Despite challenges related to data sparsity and conformational variability, ViTs show strong performance and high robustness in structure-based affinity prediction tasks, underscore their effectiveness in learning spatial patterns and suggest broader applicability to related tasks, such as protein-protein or protein-nucleic acid interaction modeling.

Zero-shot design of drug-binding proteins via neural iterative selection−expansion

This work used its strategy, termed neural iterative selection–expansion (NISE), to design proteins that, using different folds, specifically bind to two chemically distinct small-molecule drugs, exatecan and apixaban, with success rates of 100%, respectively.

Voxel-Based Deep Learning Method for Local Detection of Protein-Ligand Binding Sites

Accurate detection of ligand binding sites on proteins plays a crucial role in drug design, functional annotation, and biological classification. Deep learning (DL) models applied to computational binding site prediction have improved the characterization of binding site properties that are often difficult to obtain experimentally. However, most binding site detection DL algorithms search exhaustively around the whole protein, which is computationally expensive. Existing DL models are also sensitive to the choice of hyperparameters and architecture, which can lead to inaccurate predictions. To address these limitations, we present VoxelProt-ligand, a local voxel-based deep learning method for binding site detection in protein-ligand interactions. VoxelProt-ligand is built upon an architecture that encodes protein surfaces as sparse, octree-based 3D voxel grids. These sparse grids are used to train a 3D-CNN for classifying surface regions as binding or non-binding. Accurate position and volume of binding sites is achieved with a local search that leverages protein surface geometry and an energy score. VoxelProt-ligand is trained on the MaSIF-ligand dataset and evaluated on HOLO4K and COACH420. Our results shows close agreement with the ground truth in all evaluation datasets for the joint criteria of whether a protein contains binding sites and average number of detected binding sites per protein. Importantly, VoxelProt-ligand improves detection of binding site volume, achieving significantly higher binding pocket shape overlap than existing methods, while maintaining competitive binding site localization success rates. These results indicate that VoxelProt-ligand offers a viable path toward applications in computational drug discovery that depend on accurate determination of binding pocket geometry.