schrodinger.application.glide.ensemble_selection module¶
Classes for representing receptor ensembles and for choosing the best ensembles of a specified size from a cross-docking experiment.
Example:
from schrodinger.application.glide import ensemble_selection es = ensemble_selection.EnsembleSelection(fname=’xdock.csv’,
exp_dg_fname=’exp.txt’)# get the 10 best 3-member ensembles
print “Best by count” for ens in es.best_ensembles_by_count(3, 10):
print “%dt%.3ft%s” % (ens.count, ens.rmsd, ens)print “nBest by rmsd” for ens in es.best_ensembles_by_rmsd(3, 10):
print “%dt%.3ft%s” % (ens.count, ens.rmsd, ens)
For more details, see the documentation for the EnsembleSelection class.
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schrodinger.application.glide.ensemble_selection.
lignum_reader
(reader, lignum_prop='i_i_glide_lignum', ligoffset=0)¶ A generator that returns the structures in fname that have lignum_prop > ligoffset. ‘reader’ is an iterator that generates Structure objects (e.g., a StructureReader). IMPORTANT NOTES: 1) This requires that the structures are sorted by lignum (as in a raw
file from Glide), and that each lignum appears once at most. An exception will be raised otherwise.- If a ligand is missing in the file, the generator returns None for that ligand. For example, if lig2 is not in the file, we get (lig1, None, lig3…)
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class
schrodinger.application.glide.ensemble_selection.
Ensemble_Priority_Queue
(nslots)¶ Bases:
object
Class implementing a priority queue for the purpose of maintaining lists of ensembles in memory during ensemble selection
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__init__
(nslots)¶ Initialize self. See help(type(self)) for accurate signature.
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add
(ensemble_tuple)¶
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pop
()¶
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best_scoring_element
()¶
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worst_saved_score
()¶
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pushpop
(ensemble_tuple)¶
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length
¶
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ensembles
¶
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__class__
¶ alias of
builtins.type
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__delattr__
¶ Implement delattr(self, name).
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__dict__
= mappingproxy({'__module__': 'schrodinger.application.glide.ensemble_selection', '__doc__': ' Class implementing a priority queue for the purpose\n of maintaining lists of ensembles in memory during\n ensemble selection ', '__init__': <function Ensemble_Priority_Queue.__init__>, 'add': <function Ensemble_Priority_Queue.add>, 'pop': <function Ensemble_Priority_Queue.pop>, 'best_scoring_element': <function Ensemble_Priority_Queue.best_scoring_element>, 'worst_saved_score': <function Ensemble_Priority_Queue.worst_saved_score>, 'pushpop': <function Ensemble_Priority_Queue.pushpop>, 'length': <property object>, 'ensembles': <property object>, '__dict__': <attribute '__dict__' of 'Ensemble_Priority_Queue' objects>, '__weakref__': <attribute '__weakref__' of 'Ensemble_Priority_Queue' objects>})¶
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__dir__
() → list¶ default dir() implementation
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__eq__
¶ Return self==value.
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__format__
()¶ default object formatter
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__ge__
¶ Return self>=value.
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__getattribute__
¶ Return getattr(self, name).
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__gt__
¶ Return self>value.
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__hash__
¶ Return hash(self).
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__init_subclass__
()¶ This method is called when a class is subclassed.
The default implementation does nothing. It may be overridden to extend subclasses.
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__le__
¶ Return self<=value.
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__lt__
¶ Return self<value.
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__module__
= 'schrodinger.application.glide.ensemble_selection'¶
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__ne__
¶ Return self!=value.
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__new__
()¶ Create and return a new object. See help(type) for accurate signature.
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__reduce__
()¶ helper for pickle
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__reduce_ex__
()¶ helper for pickle
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__repr__
¶ Return repr(self).
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__setattr__
¶ Implement setattr(self, name, value).
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__sizeof__
() → int¶ size of object in memory, in bytes
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__str__
¶ Return str(self).
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__subclasshook__
()¶ Abstract classes can override this to customize issubclass().
This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).
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__weakref__
¶ list of weak references to the object (if defined)
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