ab_fep_active_learning.py evaluate Command Help
Command: $SCHRODINGER/run -FROM scisol ab_fep_active_learning.py evaluate
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usage: ab_fep_active_learning.py evaluate [-h]
[-infile_list_file <infile_path_list>]
[-block_size <num_lig_per_block>]
[-smi_index <smiles_column_index>]
[-name_index <title_column_index>]
[-no_header]
[-result_prefix <output_file_prefix>]
[-remote_input_ligands]
[-restart_file <restart_pkl_file>]
[-avoid_splitting_csv_files]
[-infile INFILE]
[-props_filter_file <file>]
[-receptor <receptor>]
[-grid <gridfile>]
[-glide_ws_model <glide_ws_model_file>]
[-jobname <jobname>]
[-stop_after <stop_workflow_after_stage>]
[-max_ml_eval_cpu <maximum_ml_evaluation_cpu>]
[-eval_with_mq] [-use_autoqsar]
[-fep_gpu_host <FEP_JOBS_GPU_HOST>]
[-max_fep_subjob_restarts <maximum_number_retries_fep_subjob>]
[-max_ligprep_cpu <maximum_ligprep_cpu>]
[-max_ffbuilder_cpu <maximum_ffbuilder_cpu>]
[-max_fep_subjobs <maximum_fep_subjobs>]
[-max_concurrent_fep_subjobs <maximum_concurrent_fep_subjobs>]
[-model <ml_model.qzip>]
[-random_seed <random_seed_number>]
[-overwrite_args] [-force_restart]
[-ligprep_args <ligprep_arguments>]
[-need_ligprep]
[-fep_job_batch_size <ligands_per_fep_job>]
[-opls_dir <opls_ff_dir>]
[-fep_restraints_file <fep_restraints_file>]
[-optimization_target ml_optimization_property]
[-use_graphdb]
[-graphdb_proj <graphdb_project_name>]
[-standalone_ffbuilder]
[-max_docking_cpu <maximum_docking_cpu>]
[-train_with_glide_feature]
[-feature_options <feature_apply_options>]
[-glide_mq]
[-extra_docking_inputs <input_file_of_extra_docking_inputs>]
[-need_dock]
[-poses_per_lig <docking_poses_per_lig>]
[-abfep_args <abfep_arguments>]
[-abfep_charged_ppj <abfep_charged_ppj>]
[-abfep_json_args_file]
[-final_rescore_abfep_args <final_rescore_job_abfep_arguments>]
[-final_rescore_abfep_json_args_file]
[-final_rescore_abfep_charged_ppj <final_rescore_abfep_charged_ppj>]
[-num_final_rescore_ligand <num_final_rescore_ligand>]
[-keep <num_returned_ligand> | -keep_fraction <fraction_of_returned_ligand>]
[-num_rescore_ligand <num_rescore_ligand> | -rescore_ligand_fraction <fraction_of_rescore_ligand>]
options:
-h, --help show this help message and exit
-random_seed <random_seed_number>
Random seed number for shuffling all the ligands
and seeding ligand_ml training.
-overwrite_args Overwrite previous arguments, Default is False.
-force_restart Force the workflow to restart when some restarting files are missing.
-ligprep_args <ligprep_arguments>
Arguments for using ligprep to prepare the ligands. Default is '-pht 1.0 -epik -s16'.
-need_ligprep Run ligprep on input ligands.
-fep_job_batch_size <ligands_per_fep_job>
Number of ligands per FEP job. Default is 100.
-opls_dir <opls_ff_dir>
Path of the OPLS force field directory.
-fep_restraints_file <fep_restraints_file>
Apply positional restraints to the receptor as specified
in the provided file. Only available for AL-FEP+ and AL-ABFEP.
The file should give the restraints in Ark format,
e.g., a file could contain: restraints={existing=ignore
new=[{atoms='asl:(protein and backbone and not a.ele H)'
force_constants=[1.0 2.0 3.0] name=posre_harm}]}
-optimization_target ml_optimization_property
Optimization target of the workflow.
Supported target:
*dG -> Optimize dG value
*LE -> Optimize ligand efficiency
*LLE -> Optimize ligand lipophilicity efficiency
*AEI -> Optimize ADME efficiency index
Default value is dG.
-use_graphdb Run FEP on GraphDB.
-graphdb_proj <graphdb_project_name>
Name of the GraphDB project for running FEP plus job.
-standalone_ffbuilder
Do not use graphdb's ffbuilder to run forcefield calculations.
Uses standalone ffbuilder. Default is False.
-max_docking_cpu <maximum_docking_cpu>
Maximum number of CPUs for Glide SIP jobs.
-train_with_glide_feature
Include Glide per residual score as additional features in ML model building.
Ligands supplied by -known_fep_dg will not be included in the initial training
if this flag is on. Residue interactions are obtained using Glide's score-in-place method.
-feature_options <feature_apply_options>
A space-separated string that configures the set of features used during training.
Trains separate models for each specified option and selects the best
performing model based on R2 score,
no_feature -> Train using only default ligand features.
additionally derived features will not be used.
only_feature -> Train using only additionally derived features.
ligand features will not be used.
with_feature -> Train using both additionally derived features and
ligand features.
Example:
-feature_options with_feature no_feature only_feature
Default value is no_feature.
-glide_mq Turn on Glide execution with ZMQ.
-extra_docking_inputs <input_file_of_extra_docking_inputs>
a file with additional Glide keywords and values for generating input poses.
-need_dock Generate input pose with Glide docking or Glide WS docking.
-poses_per_lig <docking_poses_per_lig>
Number of poses generated from Glide docking.
Glide WS docking only supports single docking pose.
Default value is 1.
-abfep_args <abfep_arguments>
Arguments string for ABFEP jobs.
Default is '-ppj 1 -md-sim-time 1000 -fep-sim-time 5000 -time-complex 5000 -time-solvent 5000'
-abfep_charged_ppj <abfep_charged_ppj>
'-ppj' value used only for charged compounds
when running ABFEP with job control. Neutral
compounds use '-ppj' from -abfep_args. Allows charged
compounds to use more processors per job.
If unset, all compounds use the '-ppj' from -abfep_args.
-abfep_json_args_file
.json file that contains the ABFEP running arguments.
The .json file should be parsable by json.load() as:
{
"arg": "value",
}
The arguments specified in this file will override the args string specified in command line.
-final_rescore_abfep_args <final_rescore_job_abfep_arguments>
Arguments string for ABFEP jobs in final rescore stage. Final
rescore stage picks top num_final_rescore_ligand ligands from
the initial ABFEP runs to run another round of ABFEP with
these arguments for potentially more accurate results.
This or final_rescore_abfep_json_args_file must be specified
if num_final_rescore_ligand > 0.
-final_rescore_abfep_json_args_file
.json file that contains the ABFEP running arguments for
rescore stage. The .json file should parsable by json.load() as
{
"arg": "value",
}
The arguments specified in this file will override the args string specified in command line.
Default is the same value as final_rescore_abfep_args.
-final_rescore_abfep_charged_ppj <final_rescore_abfep_charged_ppj>
'-ppj' value used only for charged compounds in the final
rescore stage when running ABFEP with job control. Neutral
compounds use '-ppj' from -final_rescore_abfep_args.
If unset, all final rescore compounds use the '-ppj' from
-final_rescore_abfep_args.
-num_final_rescore_ligand <num_final_rescore_ligand>
Select number of the top ligands to run based on results of -abfep_args.
Default is 0.
-keep <num_returned_ligand>
Number of best ligands to be returned. Default is 100000.
-keep_fraction <fraction_of_returned_ligand>
Fraction of the ligands to be returned.
-num_rescore_ligand <num_rescore_ligand>
Number of the best ligands predicted by the last
ML model to run rescore with FEP+ single edge or
AB-FEP. Default is 1000
-rescore_ligand_fraction <fraction_of_rescore_ligand>
Fraction of the best ligands to run FEP rescore (single edge for small molecule FEP).
file options:
-infile_list_file <infile_path_list>
A file that contains a list of infile paths.
-block_size <num_lig_per_block>
Number of ligands in each sub input ligands file. Default is
15000 for AL-FEP+/AL-ABFEP and 100,000 for AL-Glide.
-smi_index <smiles_column_index>
1-based column index of ligand's SMILES.
Default is 1.
-name_index <title_column_index>
1-based column index of ligand's title.
Default is 2.
-no_header Whether the input file(s) has header in the first line.
-result_prefix <output_file_prefix>
prefix of the .csv result files. Default is -jobname.
-remote_input_ligands
Whether input ligand files are located at remote. Absolute
paths of input ligand files are required if this flag is
provided.
-restart_file <restart_pkl_file>
.pkl file for restarting or continuing the active learning
workflow.
-avoid_splitting_csv_files
Enable this option to use pre-split ligand CSV files, avoiding redundancy.
Requirements:
- Reorder columns: 'SMILES' first, 'Title' second.
- Each split CSV must have the same header: 'SMILES, Title'.
- Remove any additional columns.
- Optionally gzip compress each split CSV file.
- Archive all pre-split CSV files into one directory and zip compress it.
Pass the zipped directory to '-infile'.
-infile INFILE .csv, .smi or .mae file(s) that contains all the ligand
SMILES. Multiple files can be included by specifying
multiple -infile options.
-props_filter_file <file>
Path to a file containing property and / or smarts filters.
Each line defines a filter a hit must satisfy. Filters are specified as:
<property>,<operator>,<value1>,<value2>...
<property>: Name of a property. Available properties include:
• Physicochemical: ALOGP, MW, PSA, HBA, HBD
• Structural: TOTAL_RINGS, STEREO_CENTERS, FORMAL_CHARGE
• Classification: PROPERTY_CLASS
• Substructure: SMARTS (for substructure matching)
<operator>: SQL logical operator: =, <, >, <=, >=, <>, BETWEEN, LIKE, IN.
<value(s)>: Target value(s). Use exactly 2 values for BETWEEN,
1 or more for IN, and 1 for all other operators.
Examples:
AlogP,BETWEEN,2.0,5.0 # Lipophilicity range
MW,<,500 # Molecular weight limit
HBD,<=,5 # Hydrogen bond donors
STEREO_CENTERS,<,3 # Stereochemical complexity
SMARTS,IN,c1[nX2][nX3]cc1 # Substructure matching
Note: Physicochemical properties are computed and stored automatically.
-receptor <receptor> Protein receptor .mae file.
-grid <gridfile> Glide grid file for docking.
-glide_ws_model <glide_ws_model_file>
Glide WS model file for running docking jobs.
job options:
-jobname <jobname> Job name of the active learning workflow run.
-stop_after <stop_workflow_after_stage>
Terminate the workflow after the specified stage finished.
Specify FinishAll (case-insensitive) to run all the remaining stages.
For convenience, you can also specify 'iter_X' to stop after
iteration X (e.g., 'iter_1', 'iter_2', etc.).
-max_ml_eval_cpu <maximum_ml_evaluation_cpu>
Allowed maximum number of CPU for
machine learning evaluation subjobs.
-eval_with_mq Enables ligand_ml evaluation using zeroMQ.
Specify the number of evaluation jobs with -max_ml_eval_cpu.
If running a screen or pilot, the -chosen_models flag will
automatically set to TorchGraphConv models.
-use_autoqsar Use AutoQSAR instead of DeepAutoQSAR for ML model building and evaluation. Only supports AL-FEP+ and AL-ABFEP+
-fep_gpu_host <FEP_JOBS_GPU_HOST>
GPU host name for running FEP jobs. Default is the same as -train_host.
-max_fep_subjob_restarts <maximum_number_retries_fep_subjob>
Maximum number of restart attempts for per FEP job.
Default is 1estart. This option does not impact the
graphdb or FEP+ web service runs which are restarted automatically.
-max_ligprep_cpu <maximum_ligprep_cpu>
Allowed maximum number of CPUs for ligprep subjobs.
-max_ffbuilder_cpu <maximum_ffbuilder_cpu>
Allowed maximum number of CPUs for force field builder subjobs.
-max_fep_subjobs <maximum_fep_subjobs>
Allowed maximum number of FEP jobs.
-max_concurrent_fep_subjobs <maximum_concurrent_fep_subjobs>
Allowed maximum number of concurrently running FEP jobs.
evaluate options:
-model <ml_model.qzip>
Generated ligand_ml .qzip model.