schrodinger.active_learning.al_node module¶

schrodinger.active_learning.al_node.estimate_time_cost(num_ligands, num_iter, train_size, train_time, num_score_license, num_autoqsar_license, available_cpu=None, score_per_ligand_cost=20, autoqsar_per_ligand_cost=0.02, num_rescore_ligand=0, multiplier=1.0, application='')¶

Roughly estimate the time cost a active learning job based on the inputs and number of available licenses.

Parameters

num_ligands (int) – total number of ligands in the library.
num_iter (int) – number of active learning iterations.
train_size (int) – Ligand_ML training size per iteration.
train_time (float) – Ligand_ML training time per iteration in hours.
num_score_license (int) – total number of the application licenses
num_autoqsar_license (int) – total number of AutoQSAR licenses
available_cpu (int) – number of available CPU
score_per_ligand_cost (float) – estimate time of of single ligand scoring time cost in second.
autoqsar_per_ligand_cost (float) – estimate time of of single ligand Ligand_ML time cost in second.
num_rescore_ligand – Number of ligands to be rescored.
multiplier (float) – estimate expansion number per ligand.
application (str) – name of the application that provides score

Returns

estimate time cost in hour

Return type

float

schrodinger.active_learning.al_node.get_jobdj(host_list=None)¶

Return JobDJ with specified host list

Parameters: host_list ([(str, int)] or None) – A list of (<hostname>, <maximum_concurrent_subjobs>)
Returns: JobDJ with specific settings.
Return type: queue.JobDJ object

schrodinger.active_learning.al_node.get_top_ligands_from_csv_list(csv_list, output_csv, num_ligands)¶

Get the top ligands from a list of .csv files. Write the selected ligands to output csv file.

Parameters

csv_list (list(str)) – list of .csv files containing the ligands.
output_csv (str) – name of output .csv file.
num_ligands (int) – number of ligands to select.

class schrodinger.active_learning.al_node.ActiveLearningNode(iter_num=1, job_name='active_learning', job_dir='.')¶

Bases: object

__init__(iter_num=1, job_name='active_learning', job_dir='.')¶

Initialize node for active learning workflow.

Parameters

iter_num (int) – current active learning iteration number.
job_name (str) – active learning job name.
job_dir (str) – directory of where the jobs in the node will run.

classmethod getName(iter_num)¶

addOptionalRestartFiles(active_learning_job)¶

Add node’s optional restart file(s) to driver’s restart dict. Dump the restart dict to the restart .pkl file.

Parameters: active_learning_job (ActiveLearningJob instance) – current AL driver

class schrodinger.active_learning.al_node.PrepareSmilesNode(args, iter_num, job_name, job_dir)¶

Bases: schrodinger.active_learning.al_node.ActiveLearningNode

__init__(args, iter_num, job_name, job_dir)¶: Initialize node for selecting ligands (SMILES) to be scored by ScoreProviderNode.

checkOutcome(smi_file)¶

Validate the generated SMILES file.

Parameters: smi_file (str) – name of SMILES file to be validated.

runNode(csv_list, active_learning_job, smi_file_name=None, **kwargs)¶

Select ligands to be scored.

Parameters

csv_list (list(str)) – list of csv files that contain candidate ligands.
active_learning_job (ActiveLearningJob instance.) – current active learning job.
smi_file_name (str) – SMILES file name that contains selected ligands.

uncertaintySelect(smi_file_name, scored_csv_file_list, sample_size, **kwargs)¶

Select random ligands from initial input csv or ligands with largest uncertainty from sorted ligand_ml .csv output.

Parameters

smi_file_name (str) – SMILES file name that contains selected ligands.
scored_csv_file_list (list(str)) – list of ligand_ml training .csv file.
sample_size (int) – number of ligands to be sampled.

greedySelect(smi_file_name, scored_csv_file_list, sample_size, ascending=True, **kwargs)¶

Select top ligands from sorted ligand_ml .csv output. ligands in self.csv_list should be already sorted from best to worst.

Parameters

smi_file_name (str) – SMILES file name that contains selected ligands.
scored_csv_file_list (list(str)) – list of .csv files containing scored ligands
sample_size (int) – number of ligands to be sampled.
ascending (bool) – ligands with lower scores are better

randomSelect(smi_file_name, scored_csv_file_list, sample_size, sort=True, **kwargs)¶

Select sample_size random ligands from input csv file(s).

Parameters

smi_file_name (str) – SMILES file name that contains selected ligands.
scored_csv_file_list (list(str)) – list of ligand_ml training .csv file.
sample_size (int) – number of ligands to be sampled.
sort (bool) – Whether the csv files were sorted or initial inputs.

diversitySelect(smi_file_name, scored_csv_file_list, sample_size, sort=True, **kwargs)¶

Use combinatorial_diversity to select diverse ligands from input csv or sorted ligand_ml .csv output.

Parameters

smi_file_name (str) – SMILES file name that contains selected ligands.
scored_csv_file_list (list(str)) – list of ligand_ml training .csv file.
sample_size (int) – number of ligands to be sampled.
sort (bool) – Whether the csv files were sorted or initial inputs..

addOptionalRestartFiles(active_learning_job)¶

Add node’s optional restart file(s) to driver’s restart dict. Dump the restart dict to the restart .pkl file.

Parameters: active_learning_job (ActiveLearningJob instance) – current AL driver

classmethod getName(iter_num)¶

class schrodinger.active_learning.al_node.ScoreProviderNode(iter_num, job_name, job_dir)¶

Bases: schrodinger.active_learning.al_node.ActiveLearningNode

__init__(iter_num, job_name, job_dir)¶: Initialize node for obtaining the score of each ligand (SMILES).

checkOutcome(score_csv_file)¶

Validate the .csv score file.

Parameters: score_csv_file (str) – name of generated .csv score file.

writeScoreCsv(title_to_score, output_csv)¶

Write score to .csv file that ligand_ml needs for training

Parameters

title_to_score (defaultdict(lambda : BAD_SCORE)) – dict that maps ligand title to score
output_csv – ligand_ml training .csv file.
output_csv – str

addOptionalRestartFiles(active_learning_job)¶

Add node’s optional restart file(s) to driver’s restart dict. Dump the restart dict to the restart .pkl file.

Parameters: active_learning_job (ActiveLearningJob instance) – current AL driver

classmethod getName(iter_num)¶

class schrodinger.active_learning.al_node.KnownScoreProviderNode(args, iter_num, job_name, job_dir)¶

Bases: schrodinger.active_learning.al_node.ScoreProviderNode

Class for obtaining the scores from external .csv file. This class is only used for the purpose of testing the performance active learning workflow.

__init__(args, iter_num, job_name, job_dir)¶: Initialize node for obtaining the score of each ligand (SMILES).

runNode(smi_file_name, active_learning_job, score_csv_file=None)¶

Read scores from active_learning_job.known_title_to_score.

Parameters

smi_file_name (str) – SMILES file that contains the ligands to be scored.
active_learning_job (ActiveLearningJob instance.) – current active learning job.
score_csv_file (str) – ligand_ml training .csv file.

addOptionalRestartFiles(active_learning_job)¶

Add node’s optional restart file(s) to driver’s restart dict. Dump the restart dict to the restart .pkl file.

Parameters: active_learning_job (ActiveLearningJob instance) – current AL driver

checkOutcome(score_csv_file)¶

Validate the .csv score file.

Parameters: score_csv_file (str) – name of generated .csv score file.

classmethod getName(iter_num)¶

writeScoreCsv(title_to_score, output_csv)¶

Write score to .csv file that ligand_ml needs for training

Parameters

title_to_score (defaultdict(lambda : BAD_SCORE)) – dict that maps ligand title to score
output_csv – ligand_ml training .csv file.
output_csv – str

class schrodinger.active_learning.al_node.LigandMLTrainNode(args, iter_num, job_name, job_dir)¶

Bases: schrodinger.active_learning.al_node.ActiveLearningNode

Class for ligand_ml model generation.

__init__(args, iter_num, job_name, job_dir)¶

Initialize node for active learning workflow.

Parameters

iter_num (int) – current active learning iteration number.
job_name (str) – active learning job name.
job_dir (str) – directory of where the jobs in the node will run.

checkOutcome(model_file)¶

Check whether ligand_ml model exist.

Parameters: model_file (str) – name of ligand_ml .qzip model file

createTrainingCsvFile(discard_cutoff, ascending=True)¶

Generate .csv file for ligand_ml training

Parameters

discard_cutoff (float) – score cutoff for excluding the ligands in ML training set.
ascending (bool) – lower value means better ligand if ascending is True

Generate training .csv file for ligand_ml model generation.

runNode(active_learning_job)¶

Perform ligand_ml training with all the scored ligands. The model file includes the job_args.json file

Parameters: active_learning_job (ActiveLearningJob instance.) – current active learning job.

runLigandMLMerge(merged_model_name, sub_model_list, jobdj=None)¶

Merge list of .tar.gz ligand_ml models to single .tar.gz ligand_ml model.

Parameters

merged_model_name (str) – path of the final merged .tar.gz model.
sub_model_list (list(str)) – list of to be merged ligand_ml models.
jobdj (queue.JobDJ object or None) – JobDJ where the merging job runs.

addOptionalRestartFiles(active_learning_job)¶

Add node’s optional restart file(s) to driver’s restart dict. Dump the restart dict to the restart .pkl file.

Parameters: active_learning_job (ActiveLearningJob instance) – current AL driver

classmethod getName(iter_num)¶

class schrodinger.active_learning.al_node.LigandMLEvalNode(args, iter_num, job_name, job_dir)¶

Bases: schrodinger.active_learning.al_node.ActiveLearningNode

Class for performing ligand_ml prediction with generated model.

__init__(args, iter_num, job_name, job_dir)¶

Initialize node for active learning workflow.

Parameters

iter_num (int) – current active learning iteration number.
job_name (str) – active learning job name.
job_dir (str) – directory of where the jobs in the node will run.

getBestResults(file_list, outfile, ascending=True)¶

Get the best ligands (with lowest score) predicted by ligand_ml.

Parameters

file_list (list(str)) – list of ligand_ml .csv output files. Each file is sorted by ligand_ml prediction score.
outfile (str) – .csv file that contains best ligands.
ascending (bool) – lower value means better ligand if ascending is True

checkOutcome(pred_csv_list, uncertain_csv_list)¶

Check the existence of ligand_ml prediction files.

Parameters

pred_csv (list(str)) – list of ligand_ml prediction csv file(s)
uncertain_csv (list(str)) – list of ligand_ml prediction with uncertainty csv file(s).

runNode(model_file, active_learning_job)¶

Use the trained model to evaluate all the ligands.

Parameters

model_file – ligand_ml .qzip model file.
model_file – str
active_learning_job (ActiveLearningJob instance.) – current active learning job.

addOptionalRestartFiles(active_learning_job)¶

Add node’s optional restart file(s) to driver’s restart dict. Dump the restart dict to the restart .pkl file.

Parameters: active_learning_job (ActiveLearningJob instance) – current AL driver

classmethod getName(iter_num)¶

class schrodinger.active_learning.al_node.ActiveLearningNodeSupplier(calculate_score_node, pilot_score_node, rescore_node, score_provider_node=<class 'schrodinger.active_learning.al_node.ScoreProviderNode'>, prepare_smi_node=<class 'schrodinger.active_learning.al_node.PrepareSmilesNode'>, known_score_provider_node=<class 'schrodinger.active_learning.al_node.KnownScoreProviderNode'>, ligand_ml_train_node=<class 'schrodinger.active_learning.al_node.LigandMLTrainNode'>, ligand_ml_eval_node=<class 'schrodinger.active_learning.al_node.LigandMLEvalNode'>)¶

Bases: object

__init__(calculate_score_node, pilot_score_node, rescore_node, score_provider_node=<class 'schrodinger.active_learning.al_node.ScoreProviderNode'>, prepare_smi_node=<class 'schrodinger.active_learning.al_node.PrepareSmilesNode'>, known_score_provider_node=<class 'schrodinger.active_learning.al_node.KnownScoreProviderNode'>, ligand_ml_train_node=<class 'schrodinger.active_learning.al_node.LigandMLTrainNode'>, ligand_ml_eval_node=<class 'schrodinger.active_learning.al_node.LigandMLEvalNode'>)¶