PDBBind

PDBBind is a dataset that contains experimentally determined protein-ligand binding affinities and is used to evaluate predictive models for binding affinity in protein-ligand interactions.

Papers using PDBBind (10)

MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking2025 · 1 citesHierarchical Contrastive Learning for Multi-Domain Protein-Ligand Binding2026S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening2025Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning2025DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction2025Group Ligands Docking to Protein Pockets2025DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking2022 · 348 citesDSDP: A Blind Docking Strategy Accelerated by GPUs2023 · 36 citesCompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning2024Improving generalisability of 3D binding affinity models in low data regimes2024