schrodinger.active_learning.al_report module

schrodinger.active_learning.al_report.get_report_template(template_name: str)

Find and return an Active Learning report template.

schrodinger.active_learning.al_report.get_ligand_ml_metric(ligand_ml_model_file: str, with_model_info: bool = False) → Tuple

Extract the test set metrics, test set labels and predictions from ligand_ml model file.

Parameters:

  • ligand_ml_model_file – ligand_ml .qzip model file.

  • with_model_info – if True, return the model info as well.

Returns:

r2, mae, rmse, labels and prediction of the test set, (trained_sub_models, final_sub_models)

schrodinger.active_learning.al_report.get_autoqsar_metric(autoqsar_model_file: str) → Tuple[float, float, Dict[str, int]]
schrodinger.active_learning.al_report.make_train_report(ligand_ml_model_file: str, report_path: str, iter_num: int)

Generate a pdf file that records the test set metrics of the ligand_ml model.

Parameters:

  • ligand_ml_model_file – ligand_ml .qzip model file.

  • report_path – path of the pdf report

  • iter_num – current iteration number

schrodinger.active_learning.al_report.make_score_hist_plot(name_to_scores: Dict, x_label: str, plot_path: str)

Generate a bar-type histogram plot which contains multiple data. The names of the data are used as the legends.

Parameters:

  • name_to_scores – a dictionary where keys are used as the legends and values are the numbers to be plotted.

  • x_label – label of x-axis

  • plot_path – path of the saved plot

schrodinger.active_learning.al_report.get_image(path: str, width: float = 72.0) → Image

Convert image file to reportlab image object that has the same aspect ratio and specified width.

Parameters:

  • path – path of the image file.

  • width (float) – width of the reportlab image.

Returns:

reportlab image

schrodinger.active_learning.al_report.get_report_maker(active_learning_job)

Get corresponding report maker for the active learning job. It returns None for evaluate task since we do not have report for it yet.

Parameters:

active_learning_job (ActiveLearningJob) – active learning job to be processed.

Returns:

corresponding report maker

Return type:

ALPilotReportMaker

schrodinger.active_learning.al_report.get_time_cost(nodes: Dict, node_name: str) → str

Return the time cost of a node. It returns ‘Unavailable’ if the time cost is not available.

Parameters:

  • nodes – dict that maps node name to node object.

  • node_name – name of the active learning node of interest.

Returns:

time cost in h/m/s format.

schrodinger.active_learning.al_report.get_score_pred_as_array(title_to_score: Dict, pred_score_file: str, discard_cutoff: float, ascending: bool = True) → ndarray

Return the score, predicted score, prediction uncertainty of the ligands as the N X 3 numpy array.

Parameters:

  • title_to_score – dict that maps ligand title to score.

  • pred_score_file – path of the ligand ml prediction .csv file.

  • discard_cutoff – score cutoff for excluding the ligands in ML training set.

  • ascending – lower value means better ligand if ascending is True

Returns:

numpy array of (num_of_ligands X (score, pred, uncertain))

schrodinger.active_learning.al_report.calculate_recovery_ratio(label_pred: ndarray, top_ratio: float) → ndarray

Calculate the recovery ratio of the best ligands based on label in different numbers of the top ligands predicted by ligand_ml. More negative value means better ligand.

Parameters:

  • label_pred – array of dim number of ligands X 2 contains the (label, prediction)

  • top_ratio – top ratio of the ligands by label.

Returns:

(screen ratio, recovery ratio of top ligands defined by top_ratio) of all the ligands.

schrodinger.active_learning.al_report.plot_correlation(x: List, y: List, fname: str, x_label: str, y_label: str, title: str) → None

Generate a simple correlation scatter plot :param x_label: label of x-axis :param y_label: label of y-axis :param fname: path of the saved plot :param x: list of x values :param y: list of y values :param title: title of the plot

schrodinger.active_learning.al_report.plot_regression(y_true: ndarray, y_pred: ndarray, fname: str)

Generate regression plot. This function is sightly modified from ligand_ml/plotting.py to change the labels of axis.

Parameters:

  • y_true – 1D array test set label.

  • y_pred – 2D array of ligand_ml prediction and uncertainty

  • fname – filename to save the image

schrodinger.active_learning.al_report.plot_recovery(recovery_results: Dict, fname: str)

Generate and save recovery plot image.

Parameters:

  • recovery_results – dict that maps top ratio to the recovery ratio numpy array.

  • fname – path of the saved image.

schrodinger.active_learning.al_report.make_regress_recovery_plots(y_true: ndarray, y_pred_uncertain: ndarray, top_ratio_samples: List, regress_text: str, recovery_text: str)

Generate regression plot and recovery plot and include both in a table. Also return the recovery results for the sampled top ratios as a dict.

schrodinger.active_learning.al_report.make_recovery_table(recovery_results: Dict, screen_ratio_samples: List) → List

Generate a list of list that contains the recovery ratio for certain top ratio and screen ratio.

Parameters:

  • recovery_results – dict that maps top ratio to the recovery ratio numpy array.

  • screen_ratio_samples – list of screen ratios

Returns:

table as a list of list, table caption, largest enrichment in the table.

schrodinger.active_learning.al_report.get_conclusion_string(best_enrichment: float, job_type: str, high_enrich: int = 10, low_enrich: int = 2) → str

Return the conclusion string based on the job type and the higheest enrichment we have in the recovery ratio table.

schrodinger.active_learning.al_report.make_scaffold_report(smiles: List, counts: List, report_file: str, max_occuring_count: int = 5, scaffold_type: str = 'generic_bemis_murcko')

Makes a report containing counts and structure of the five most occuring scaffolds in the input lists of smiles and counts. Saves the report in the path specified by the report_file. One may get n most occuring scaffolds by changing the parameter ‘max_occuring_count’.

Parameters:

  • smiles – list of smiles.

  • counts – list of counts corresponding to the smiles.

  • report_file – path of the output report file.

  • max_occuring_count – number of top scaffolds to keep in the report.

schrodinger.active_learning.al_report.smiles_to_svg(smiles)

Converts a SMILES string to an SVG image.

Parameters:

(str) (smiles) – The SMILES string of the molecule.

Returns:

SVG string of the molecule.

schrodinger.active_learning.al_report.get_core_smarts_mcs(ligands_list: list[str], reference_ligand_file: str = None, num_samples: int = 500) → Optional[str]

Get the core SMARTS string from a list of ligands using MCS. If a reference ligand file is provided, the reference ligands will be included in the MCS calculation.

Parameters:

  • ligands_list – List of SMILES strings of the ligands.

  • reference_ligand_file – Path to a file containing reference ligands.

  • num_samples – Number of ligands to sample for MCS calculation.

Returns:

SMARTS string of the core or None if FindMCS fails

class schrodinger.active_learning.al_report.ALReportMaker(active_learning_job)

Bases: object

Base class for different types of AL report maker.

__init__(active_learning_job)

Initialize the report maker for an active learning job

initReport(header: str)

Initialize the report and add header information

class schrodinger.active_learning.al_report.ALPilotReportMaker(active_learning_job)

Bases: ALReportMaker

__init__(active_learning_job)

Initialize the report maker for an active learning job

report()

Function for building the report

addRunDetail()

Add job specifications and running time cost information to the report

addRecoveryResults()

Add the regression plot, recovery plot, recovery table and conclusion to the report.

class schrodinger.active_learning.al_report.ALScreenReportMaker(active_learning_job)

Bases: ALReportMaker

__init__(active_learning_job)

Initialize the report maker for an active learning job

report()

Function for building the report

addRunDetail()

Add job specifications and running time cost information to the report

addRecoveryResults()

Add the regression plot, recovery plot, recovery table and conclusion to the report.

class schrodinger.active_learning.al_report.ActiveLearningHtmlReport(al_job)

Bases: object

__init__(al_job)
property workflow_name
property scorer_name
property fep_core_smarts: Optional[str]

For FEP+ jobs, the enrichment calculator calculates enrichment based on R-groups instead of the whole molecule. The core SMARTS for the R-group calculation is either provided by the user or calculated as MCS of a random subset of ligands. We cache it so same core SMARTS is used for all iterations.

get_substruct_enrich_calculator() → SubstructEnrichCalculator

For FEP+ jobs, we use R-group based substructure enrichment if it is possible to get reasonable core SMARTS. For all other workflows, we use full molecules to calculate substructure enrichment.

get_fingerprint(smiles)
property learning_target
clean_values(df_score)

Remove the outliers from the dataframe for plotting purposes.

Parameters:

df_score – DataFrame containing the scores.

Returns:

DataFrame with outliers removed.

generateReport(running_node=None)
gatherAllScores()

Get the calculated target values from each iteration and rescore stage.

updateNodeData(running_node=None)
getFlowChartData(running_node=None)
PrepareSmilesNodeText(node, status)
PrepareSmilesNodeFigs(node)
CalculateScoreNodeFigs(node)
CalculateScoreNodeMolDisplay(node)
generate_score_hist(df_score, title='Distribution Ligand Scores')
generate_score_overlay_hist(df, title='Distribution of Scores')

Generate a histogram of scores for all iterations and rescore stage.

Parameters:

  • df – DataFrame containing scores with ‘Value’ and ‘Origin’ columns.

  • title – Title for the histogram.

Returns:

HTML string of the histogram.

getRandomLigandsPCA(random_smiles)
generatePCAFig(random_smiles, df_score, title='PCA of Fingerprints of Ligands')
KnownScoreProviderNodeText(node, status)
CalculateScoreNodeText(node, status)
AutoQSARTrainNodeResults(node)
TrainNodeText(node, status)
AutoQSARTrainNodeFigs(node)
AutoQSARTrainNodeText(node, status)
LigandMLTrainNodeResults(node)
LigandMLTrainNodeFigs(node)
LigandMLTrainNodeText(node, status)
ExternalMLInputNodeText(node, status)
ExternalMLEvalNodeText(node, status)
ExternalMLEvalNodeFigs(node)
ExternalMLEvalNodeMolDisplay(node)
MLEvalNodeMolDisplay(node)
AutoQSAREvalNodeMolDisplay(node)
LigandMLEvalNodeMolDisplay(node)
MLEvalNodeFigs(node)
AutoQSAREvalNodeFigs(node)
LigandMLEvalNodeFigs(node)
MLEvalNodeText(node, status)
AutoQSAREvalNodeText(node, status)
LigandMLEvalNodeText(node, status)
RescoreNodeFigs(node)
RescoreNodeMolDisplay(node)
RescoreNodeText(node, status)
static get_property(property_func, smi)
generate_property_hist(ligands_list, ligands_origin, property_name)