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.

static readScoredLigands(scored_csv_file_list)

Read the ligands that were already scored by ScoreProviderNode.

Parameters

scored_csv_file_list (list(str)) – list of ligand_ml training .csv files.

Returns

set of titles of the scored ligands.

Return type

set(str)

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, y_index=None, **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.

• y_index (int) – column index of values to be sorted.

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.

Parameters

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.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'>)