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.
Copyright Schrodinger, LLC. All rights reserved.
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Calculator
A class to calculate enrichment terms for a screen.
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_BasePlotter
Class on which Plotter and PercentScreenPlotter are based.
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Plotter
A class to plot multiple series of Calculator data.
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PercentScreenPlotter
A class to plot multiple series of Calculator data as %Actives Found
vs %Screen.
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ActiveDecoyFingerprintAnalyzer
Convert a structure file of screen results (or inputs) into active
and decoy fingerprints and report similarity metrics between the
fingerprint sets.
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TwoGroupFingerprintAnalyzer
Summarize the similarity differences within and between two groups
of fingerprints.
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FingerprintFromFileGenerator
Class to generate (identifier, fingerprint) items, one at a time,
from a structure file.
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StatisticalSummary
Container class to store summary metrics describing an array of
numerical values.
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schrodinger.utils.cmdline SingleDashOptionParser
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get_parser()
Returns:
A command line argument parser with application specific flags and
defaults defined. |
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logger = log.get_output_logger('schrodinger.analysis.enrichment')
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_version = '$Revision: 1.9 $' |
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SORT_CHOICES = ['descending', 'ascending', 'none'] |
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cmdline_doc = '\nCalculated metrics:\n BEDROC\n Bolt... |
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__package__ = 'schrodinger.analysis' |
- Returns: schrodinger.utils.cmdline SingleDashOptionParser
- A command line argument parser with application specific flags
and defaults defined.
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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,
...
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