diffdock

Virtual screening uses diffusion models to dock large libraries of small molecules against a specific protein target, ranking compounds by predicted binding poses and confidence scores to identify promising drug candidates.

Molecular docking with diffusion models predicts 3D ligand binding poses by iteratively refining molecular orientations against a protein structure, outputting structural conformations and confidence scores for virtual screening campaigns.

Yes, batch processing supports virtual screening campaigns by evaluating thousands of SMILES inputs against a protein target, outputting predicted binding poses and confidence scores to identify potential drug candidates.

Yes, predicting protein-ligand binding poses requires a Python environment with PyTorch, RDKit, and ESM dependencies installed to process PDB structures and SMILES inputs for diffusion model inference.

The best way to find new inhibitors is to run a virtual screening campaign by docking compound libraries against your protein PDB structure, then filtering candidates by their predicted binding poses and confidence scores.

Protein-ligand interaction predictions support PDB files for protein structures and SMILES strings for ligand inputs, enabling both single complex docking and batch virtual screening workflows.