Jun 2026· ACM International Conference on Bioinformatics, Computational Biology and Biomedicine· 0 citations· 30 references
Computer Science
Abstract
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.
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.
Jakub Poziemski, Paweł Siedlecki· Scientific Reports· 0 citations
This study shows that incorporating synthetic molecular dynamics data improves deep learning models for protein–ligand binding affinity prediction beyond static experimental structures, and highlights that dynamic synthetic datasets can enable deep learning models to outperform conventional methods such as MM-PBSA while remaining computationally efficient.
P. Agrawal, Prathit Chatterjee, U. Priyakumar· Journal of Cheminformatics· 0 citations
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.
Benjamin Fry, Kaia Slaw, Nicholas F. Polizzi· Nature· 7 citations· ⚡1
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.
Zhenqiang Zhang, Zhihao Wang, Yang Liu et al.· Journal of Chemical Informat...· 0 citations
BiteNetI is a structure-based deep learning model that uses 3D convolutional neural networks to simultaneously localize ion-binding centers and predict binding residues for 14 biologically relevant ions, supporting comprehensive and large-scale annotation of protein-ion interactions.
Igor Kozlovskii, Petr Popov· Communications Biology· 0 citations
A state-space–driven deep learning framework that leverages the efficient long-range modeling capability of the Vision Mamba architecture to learn from three-dimensional protein surfaces represented as two-dimensional geometric or physicochemical grids, establishing state-space models as efficient, interpretable, and scalable architectures for molecular surface learning.