deeprank2.tools

deeprank2.tools.target

deeprank2.tools.target.add_target(graph_path: str | list[str], target_name: str, target_list: str, sep: str = ' ') → None

Add a target to all the graphs in hdf5 files.

Parameters

• graph_path – Either a directory containing all the hdf5 files, a single hdf5 filename, or a list of hdf5 filenames.

• target_name – The name of the new target.

• target_list – Name of the file containing the data.

• sep – Separator in target list. Defaults to ” ” (single space).

Notes

The input target list should respect the following format : 1ATN_xxx-1 0 1ATN_xxx-2 1 1ATN_xxx-3 0 1ATN_xxx-4 0

deeprank2.tools.target.compute_ppi_scores(pdb_path: str, reference_pdb_path: str) → dict[str, float | int]

Compute structure similarity scores for the input docking model and return them as a dictionary.

The computed scores are: lrmsd (ligand root mean square deviation), irmsd (interface rmsd), fnat (fraction of native contacts), dockq (docking model quality), binary (True - high quality, False - low quality), capri_class (capri classification, 1 - high quality, 2 - medium, 3 - acceptable, 4 - incorrect). See https://deeprank2.readthedocs.io/en/latest/docking.html for more details about the scores.

Parameters

• pdb_path – Path to the decoy.

• reference_pdb_path – Path to the reference (native) structure.

Returns: dict containing values for lrmsd, irmsd, fnat, dockq, binary, capri_class.