Package schrodinger :: Package analysis :: Module enrichment

Module enrichment

A module for calculating Enrichment Factors and reporting on the effectiveness of a ligand database screen seeded with known actives.

The module takes information about a virtual screen outcome and calculates metrics that are commonly used to judge a screen's ability to rank known actives. The metrics include terms such as Receiver Operator Characteristic area under the curve (ROC), Enrichment Factors, and Robust Initial Enhancement. By default a report containing a suite of metrics is directed to standard out. The module can also create basic Sensitivity v 1-Specificity plots. See cmdline_doc for details on what metrics are calculated. See Class documentation and __main__ for API examples.

For most screen result input formats titles are used to identify the ligands and the input is expected to be correctly ordered. If the file contains duplicate titles then only the first occurence of a unique title is ranked. Careful consideration must be made when Glide results contain multiple titles to ensure they are properly ordered; ordering by glide score is typically *not* sufficient. For example, after saving mulitple poses per input ligand from a Glide SP and HTVS docking experiment the poses of the same chemical species should be ordered by emodel, and different species by gscore. schrodinger.structutils.sort.py has tools to facilitate proper ordering of Glide structure files.

suite2011 changes: enrichment.Calculator When parsing text active files leading whitespace is now honored such that 'ligand1' and ' ligand1' two distinct identifiers. However, trailing whitespace stripped when reading titles from a structure file and may cause problems matching values and recognizing actives.

enrichment.Calculator Canvas linear fingerprints are now calculated with the appropriate default atom/bond type. As a consequence, some DEF metrics may change slightly.

Classes

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NoActivesRankedException

Calculator
A class to calculate enrichment terms for a screen.

_BasePlotter
Class on which Plotter and PercentScreenPlotter are based.

Plotter
A class to plot multiple series of Calculator data.

PercentScreenPlotter
A class to plot multiple series of Calculator data as %Actives Found vs %Screen.

ActiveDecoyFingerprintAnalyzer
Convert a structure file of screen results (or inputs) into active and decoy fingerprints and report similarity metrics between the fingerprint sets.

TwoGroupFingerprintAnalyzer
Summarize the similarity differences within and between two groups of fingerprints.

FingerprintFromFileGenerator
Class to generate (identifier, fingerprint) items, one at a time, from a structure file.

StatisticalSummary
Container class to store summary metrics describing an array of numerical values.

Functions

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schrodinger.utils.cmdline SingleDashOptionParser

get_parser()
Returns: A command line argument parser with application specific flags and defaults defined.

Variables

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logger = log.get_output_logger('schrodinger.analysis.enrichment')

_version = '$Revision: 1.9 $'

SORT_CHOICES = ['descending', 'ascending', 'none']

cmdline_doc = '\nCalculated metrics:\n BEDROC\n Bolt...

__package__ = 'schrodinger.analysis'

Function Details

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get_parser()

Returns: schrodinger.utils.cmdline SingleDashOptionParser: A command line argument parser with application specific flags and defaults defined.

Variables Details

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cmdline_doc

Value:

'''
Calculated metrics:
    BEDROC
        Boltzmann-enhanced Discrimination Receiver Operator Characteri
stic
        area under the curve.  The value is bounded between 1 and 0,
        with 1 being ideal screen performance.  The default alpha=20
        weights the first ~8% of screen results.  When alpha*Ra << 1,
...