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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 "%d\t%.3f\t%s" % (ens.count, ens.rmsd, ens) print "\nBest by rmsd" for ens in es.best_ensembles_by_rmsd(3, 10): print "%d\t%.3f\t%s" % (ens.count, ens.rmsd, ens) For more details, see the documentation for the EnsembleSelection class.
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Ensemble_Priority_Queue Class implementing a priority queue for the purpose of maintaining lists of ensembles in memory during ensemble selection |
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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. 2) 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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