def from_smiles(self,
smiles,
identifier=None,
generate_initial_sites=True):
if identifier is None:
identifier = smiles
if generate_initial_sites:
parameter_files = _CSDDatabaseLocator.get_conformer_parameter_file_location(
)
molmaker = ConformerGeneratorLib.MoleculeTo3D(parameter_files)
mol = molecule.Molecule(identifier,
molmaker.create_conformation(smiles))
else:
molmaker = ChemicalAnalysisLib.SMILESMoleculeMaker()
mol = molecule.Molecule(identifier,
_molecule=molmaker.siteless_atoms(smiles))
return mol
def generate_fitting_points(self,
hr,
volume=400,
threshold=17,
mode='threshold'):
"""
uses the Fragment Hotspot Maps to generate GOLD fitting points.
GOLD fitting points are used to help place the molecules into the protein cavity. Pre-generating these fitting
points using the Fragment Hotspot Maps helps to biast results towards making Hotspot interactions.
:param `hotspots.result.Result` hr: a Fragment Hotspot Maps result
:param int volume: volume of the occupied by fitting points in Angstroms ^ 3
:param float threshold: points above this value will be included in the fitting points
:param str mode: 'threshold'- assigns fitting points based on a score cutoff or 'bcv'- assigns fitting points from best continuous volume analysis (recommended)
"""
temp = tempfile.mkdtemp()
mol = molecule.Molecule(identifier="fitting_pts")
if mode == 'threshold':
dic = hr.super_grids["apolar"].grid_value_by_coordinates(
threshold=threshold)
elif mode == 'bcv':
extractor = Extractor(hr=hr, volume=volume, mode="global")
bv = extractor.extracted_hotspots[0].best_island
dic = bv.grid_value_by_coordinates(threshold=17)
else:
raise TypeError(
"{} not supported, see documentation for details".format(mode))
for score, v in dic.items():
for pts in v:
atm = molecule.Atom(atomic_symbol='C',
atomic_number=14,
label='{:.2f}'.format(score),
coordinates=pts)
atm.partial_charge = score
mol.add_atom(atom=atm)
fname = os.path.join(temp, 'fit_pts.mol2')
with io.MoleculeWriter(fname) as w:
w.write(mol)
self.fitting_points_file = fname