schrodinger.application.desmond.kinetics.mtd_cleanup module

Unbinding Kinetics Cleanup workflow for infrequent MtD subjobs. Generate reports and copy one subjob cms/xtc that best matches the predicted tau.

class schrodinger.application.desmond.kinetics.mtd_cleanup.CleanUp(chkpt_file: str, asl_receptor: str, asl_ligand: str, mtd_reports: bool, residence_time_cutoff: float = 1000000000000.0, frac_unbound_thresholds: Optional[List[float]] = None, p_value_thresholds: Optional[List[float]] = None)

Bases: object

Class for cleaning up unbinding kinetics mtd subjobs.

TODO: Return the traj for the subjob closes to the predicted value

(DESMOND-12822)

__init__(chkpt_file: str, asl_receptor: str, asl_ligand: str, mtd_reports: bool, residence_time_cutoff: float = 1000000000000.0, frac_unbound_thresholds: Optional[List[float]] = None, p_value_thresholds: Optional[List[float]] = None)

property exec_dir

run()

Run the cleanup workflow.

write_unbinding_paths_summary(results_df: pandas.core.frame.DataFrame)

If unbinding paths are found, write summary structure showing sampled paths

and their associated path CVs.

write_unbinding_statistics(results_df: pandas.core.frame.DataFrame)

This function writes an ‘result’ file ($JOBNAME.dat) which is a fixed-width table with jobname and simulation resuls.

calculate_unbinding_statistics() → pandas.core.frame.DataFrame

read_archive_data()

Extract files from tgz archives

schrodinger.application.desmond.kinetics.mtd_cleanup.parse_cmd(cmdline: List[str]) → argparse.Namespace

schrodinger.application.desmond.kinetics.mtd_cleanup.main(cmdline=None)