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

needsHistogram()

Whether we can generate a histogram plot of calculated target scores.

Returns

whether the histogram of score can be plotted

Return type

bool

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.

Number of cpus and ndim are scaled proportionately to the number of random ligands selected. ndim is the dimensionality of the chemical space. When the number of random ligands is equal to the max_diversity_sample_size, ncpu and ndim scale to their maximums: 300, 13, respectively. If 300 cpus are not available, the user defined ncpus are used.

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..

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

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.

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.

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 the 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 the 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.

evalMQ(tar_model_file, output_csv, active_learning_job)

Evaluate ligands with ligand_ml model using ZMQ. Distributes evaluation jobs over a set of workers. Reverts to jobdj if ZMQ fails.

Parameters

  • tar_model_file (str) – trained tar.gz model file

  • output_csv (str) – output prediction file

  • active_learning_job (ActiveLearningJob instance.) – current active learning job.

Returns

True if ZMQ job is successful

Return type

bool

evalDJ(tar_model_file, output_csv, active_learning_job)

Evaluate ligands with ligand_ml model using jobdj.

Parameters

  • tar_model_file (str) – tar.gz trained model file

  • output_csv (str) – output prediction file

  • active_learning_job (ActiveLearningJob instance.) – current active learning job.

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'>)