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Molecular Docking

Predicting the binding structure of a small molecule ligand to a protein, which is critical to drug design.

Description from: DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Papers

Showing 125 of 128 papers

TitleStatusHype
Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose PredictionCode5
Atomic Convolutional Networks for Predicting Protein-Ligand Binding AffinityCode3
Modeling Molecular Structures with Intrinsic Diffusion ModelsCode3
DiffDock: Diffusion Steps, Twists, and Turns for Molecular DockingCode3
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete LatentsCode2
PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket ConditioningCode2
Fast and Accurate Blind Flexible DockingCode2
PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross DockingCode2
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose GenerationCode2
QuickBind: A Light-Weight And Interpretable Molecular Docking ModelCode1
CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and SolutionsCode1
Assigning Confidence to Molecular Property PredictionCode1
Domain-Agnostic Molecular Generation with Chemical FeedbackCode1
Protein language models are performant in structure-free virtual screeningCode1
SHREC 2022: Protein-ligand binding site recognitionCode1
PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular DockingCode1
DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand DockingCode1
CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-TuningCode1
DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular DockingCode1
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded LibrariesCode1
Equivariant Scalar Fields for Molecular Docking with Fast Fourier TransformsCode1
Direct Molecular Conformation GenerationCode1
DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representationsCode1
DOCKSTRING: easy molecular docking yields better benchmarks for ligand designCode1
Enhancing Ligand Pose Sampling for Molecular DockingCode1
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