diffdock

Diffusion-based molecular docking predicts protein-ligand binding poses by generating plausible 3D structures from PDB or sequence inputs and SMILES ligands, providing confidence-scored rankings for virtual screening.

Yes, you can run batch virtual screening by providing multiple protein-ligand complexes via a CSV workflow, which generates confidence-scored pose predictions to help prioritize candidates across many targets.

Structure prediction accepts protein inputs as PDB files or raw protein sequences via ESMFold, and ligand inputs as SMILES strings or structural file formats including SDF and MOL2.

No, the confidence score indicates pose prediction reliability rather than binding affinity, meaning it estimates the certainty of the generated 3D binding pose for downstream validation.

To predict binding poses with only a protein sequence, the diffusion docking pipeline utilizes ESMFold to predict the protein structure first, then proceeds to generate ligand binding poses.

The best way to prioritize candidates after structure-based screening is to rank the generated 3D docking poses using their associated confidence scores, prioritizing high-confidence predictions for downstream scoring.