diffdock

To predict protein-ligand binding poses, you can use deep learning models to dock small molecule ligands to protein targets. This approach provides high accuracy 3D binding pose predictions and confidence scores for structure-based drug discovery.

Yes, you can run molecular docking with only a protein sequence. The tool supports processing protein sequences via ESMFold to generate the required structures, alongside accepting standard PDB files for ligand binding pose prediction.

You can perform batch virtual screening by processing multiple protein-ligand pairs in batch mode. This allows you to evaluate numerous small molecule ligands against protein targets efficiently for lead optimization campaigns.

Molecular docking with deep learning provides confidence scores to assess prediction reliability. These scores help evaluate the accuracy of predicted 3D binding poses for small molecule ligands during virtual screening.

To run deep learning molecular docking, you need dependencies including torch, torch-geometric, rdkit, esmfold, and diffdock. These tools facilitate protein structure processing and ligand binding pose prediction.

The best way to dock a ligand to a protein target is by running a python inference command with specified protein path and ligand parameters. This outputs predicted 3D binding poses and confidence scores to a designated directory.