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Blind Docking On Pdbbind

Metrics

Top-1 RMSD (%u003c2)
Top-1 RMSD (Med.)

Results

Performance results of various models on this benchmark

Paper Title
FABind+43.5-FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
DIFFDOCK (40)38.2±1.03.30±0.11DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
DIFFDOCK (10)35.0±1.43.56±0.05DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
FABind33.1-FABind: Fast and Accurate Protein-Ligand Binding
EQUIBIND+GNINA 28.8-DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
EQUIBIND+SMINA23.2 6.5EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
GNINA22.97.7DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
GLIDE21.89.3DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
QVINAW 20.9-DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
P2RANK+SMINA 20.4 6.9DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
SMINA 18.77.1 DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
EQUIBIND5.5 6.2EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
TANKBIND-4.0DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
P2RANK+GNINA-5.5DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
0 of 14 row(s) selected.