Predict 3D protein-ligand binding poses with per-pose confidence scores.
npx skills add https://github.com/fuzzy-dynamics/strings --skill diffdock-fuzzy-dynamics
Please help me install this Agent Skill. Skill: diffdock Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/diffdock Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill diffdock-fuzzy-dynamics
DiffDock provides diffusion-model based docking to generate plausible 3D protein-ligand poses and accompanying confidence estimates, enabling structure-based drug design workflows without manual docking trials.
Predicts 3D binding poses for small molecules with protein targets.
Generates per-pose confidence scores to enable robust ranking and downstream scoring.
Supports single complex docking, batch docking, and virtual screening; inputs include PDB structures or sequences (via ESMFold) and ligands as SMILES or structure files (SDF/MOL2).
Enables ensemble docking for protein flexibility and straightforward integration with downstream scoring pipelines.
Use Case: Screen a library of SMILES against a target protein to prioritize top poses for experimental validation.
Run inference with the default configuration to generate poses and review the top predictions.
High-intent search queries and answers about installing and using this skill.
Predict 3D protein-ligand binding poses by inputting protein sequences via ESMFold and ligands as SMILES strings. DiffDock generates plausible 3D binding poses with accompanying per-pose confidence scores for ranking.
Batch molecular docking and virtual screening are supported natively. You can screen a library of SMILES against a target protein to prioritize top poses for experimental validation using generated confidence scores.
Diffusion-model based docking generates plausible 3D protein-ligand binding poses by iteratively refining random noise. It outputs per-pose confidence scores to enable robust ranking and integration with downstream scoring pipelines.
DiffDock works with PDB structures or sequences for protein targets, and accepts ligands as SMILES or structure files including SDF and MOL2. This flexibility supports various structure-based drug design workflows.
Handle protein flexibility during molecular docking by utilizing ensemble docking capabilities. This approach generates multiple plausible 3D binding poses to account for target conformational variations.
RDKit and pandas are required dependencies for running pose prediction workflows. They handle molecular data parsing and dataframe operations to support DiffDock's configurable inference and batch processing.
Yezez9Predict protein-ligand binding poses and confidence scores from PDB files or SMILES.
K-Dense-AIPredicts protein-ligand binding poses from PDB or sequence and SMILES inputs.
SologaPredict 3D ligand-protein binding poses from PDB files using diffusion models.
OwnLabAIGenerate protein-ligand docking poses with diffusion models and confidence scores.
