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.

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.
  1. 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…)
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

__init__(nslots)

Initialize self. See help(type(self)) for accurate signature.

add(ensemble_tuple)
pop()
best_scoring_element()
worst_saved_score()
pushpop(ensemble_tuple)
length
ensembles
__class__

alias of builtins.type

__delattr__

Implement delattr(self, name).

__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>})
__dir__() → list

default dir() implementation

__eq__

Return self==value.

__format__()

default object formatter

__ge__

Return self>=value.

__getattribute__

Return getattr(self, name).

__gt__

Return self>value.

__hash__

Return hash(self).

__init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__

Return self<=value.

__lt__

Return self<value.

__module__ = 'schrodinger.application.glide.ensemble_selection'
__ne__

Return self!=value.

__new__()

Create and return a new object. See help(type) for accurate signature.

__reduce__()

helper for pickle

__reduce_ex__()

helper for pickle

__repr__

Return repr(self).

__setattr__

Implement setattr(self, name, value).

__sizeof__() → int

size of object in memory, in bytes

__str__

Return str(self).

__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).

__weakref__

list of weak references to the object (if defined)