def join_neighboring_mols(self, mol_A: Chem.Mol, mol_B: Chem.Mol):
"""
Joins two molecules by first calling _find_closest to find closest.
That method does all the thinking.
then by calling _join_atoms.
:param mol_A:
:param mol_B:
:return:
"""
# get closets atoms
combo, candidates = self._find_all_closest(
mol_A, mol_B) # _find_all_closest is in communal
anchor_A, anchor_B, distance = candidates[0]
mol = self._join_atoms(combo,
anchor_A,
anchor_B,
distance,
linking=True)
for anchor_A, anchor_B, distance in candidates[1:]:
mol = self._join_atoms(combo,
anchor_A,
anchor_B,
distance,
linking=False)
mol.SetProp('_Name',
mol_A.GetProp('_Name') + '~' + mol_B.GetProp('_Name'))
return mol
def merge_pair(self,
scaffold: Chem.Mol,
fragmentanda: Chem.Mol,
mapping: Optional = None) -> Chem.Mol:
"""
To specify attachments use ``.merge``.
To understand what is going on see ``.categorise``
:param scaffold: mol to be added to.
:param fragmentanda: mol to be fragmented
:param mapping: see ``get_positional_mapping``. Optional in _pre_fragment_pairs
:return:
"""
done_already = []
if self._debug_draw:
print('Scaffold')
self.draw_nicely(scaffold)
print('To be added')
self.draw_nicely(fragmentanda)
fp = self._pre_fragment_pairs(scaffold, fragmentanda, mapping)
# confusingly these are hit indexed.
for anchor_index, attachment_details in fp.items():
# anchor index is the fragment-to-added's internal atom that attaches
if anchor_index in done_already:
continue
# fix rings.
uniques = {
atom.GetIdx()
for atom in fragmentanda.GetAtoms()
if 'overlapping' not in atom.GetProp('_Category')
}
team = self._recruit_team(fragmentanda, anchor_index, uniques)
other_attachments = list((team & set(fp.keys())) - {anchor_index})
other_attachment_details = []
for other in other_attachments:
other_attachment_details.append(fp[other])
done_already.append(other)
scaffold = self._merge_part(
scaffold,
fragmentanda,
anchor_index=anchor_index,
attachment_details=attachment_details,
other_attachments=other_attachments,
other_attachment_details=other_attachment_details)
name_A = scaffold.GetProp('_Name')
name_B = fragmentanda.GetProp('_Name')
scaffold.SetProp('_Name', f'{name_A}-{name_B}')
if self._debug_draw:
print('Merged', scaffold.GetProp('_Name'))
self.draw_nicely(scaffold)
return scaffold
def from_mol(cls, mol: Chem.Mol):
field_names = fields(cls)
kwargs = {}
for field in field_names:
field_name = cls._convert_field_to_sdf_field(field.name)
val = mol.GetProp(field_name)
val = field.type(val)
kwargs[field.name] = val
return RABFEResult(**kwargs)
def origin_from_mol(self, mol: Chem.Mol = None):
"""
these values are stored from Monster for scaffold, chimera and positioned_mol
:param mol: Chem.Mol
:return: stdev list for each atom
"""
if mol is None:
mol = self.positioned_mol
if mol.HasProp('_Origins'):
return json.loads(mol.GetProp('_Origins'))
origin = []
for atom in mol.GetAtoms():
if atom.HasProp('_Origin'):
x = atom.GetProp('_Origin')
if x == 'none':
origin.append([])
else:
origin.append(json.loads(x))
else:
origin.append([])
return origin
def template_sorter(t: Chem.Mol) -> int:
n_atoms = max([
len([k for k in m if k not in accounted_for])
for m in self.maps[t.GetProp('_Name')]
])
return n_atoms
def simulate_pair(epoch: int, blocker: Chem.Mol, mol: Chem.Mol):
verify_rabfe_pair(mol, blocker)
mol_name = mol.GetProp("_Name")
# generate the core_idxs
core_idxs = setup_relative_restraints_by_distance(mol, blocker)
mol_coords = get_romol_conf(mol) # original coords
num_complex_atoms = complex_coords.shape[0]
num_solvent_atoms = solvent_coords.shape[0]
# Use core_idxs to generate
R, t = rmsd.get_optimal_rotation_and_translation(
x1=complex_ref_x0[num_complex_atoms:][
core_idxs[:, 1]], # reference core atoms
x2=mol_coords[core_idxs[:, 0]], # mol core atoms
)
aligned_mol_coords = rmsd.apply_rotation_and_translation(
mol_coords, R, t)
ref_coords = complex_ref_x0[num_complex_atoms:]
complex_host_coords = complex_ref_x0[:num_complex_atoms]
complex_box0 = complex_ref_box0
solvent_host_coords = solvent_ref_x0[:num_solvent_atoms]
solvent_box0 = solvent_ref_box0
# compute the free energy of swapping an interacting mol with a non-interacting reference mol
complex_decouple_x0 = minimizer.minimize_host_4d(
[mol, blocker_mol],
complex_system,
complex_host_coords,
forcefield,
complex_box0,
[aligned_mol_coords, ref_coords],
)
complex_decouple_x0 = np.concatenate(
[complex_decouple_x0, aligned_mol_coords, ref_coords])
# compute the free energy of conversion in complex
complex_conversion_x0 = minimizer.minimize_host_4d(
[mol],
complex_system,
complex_host_coords,
forcefield,
complex_box0,
[aligned_mol_coords],
)
complex_conversion_x0 = np.concatenate(
[complex_conversion_x0, aligned_mol_coords])
min_solvent_coords = minimizer.minimize_host_4d([mol], solvent_system,
solvent_host_coords,
forcefield,
solvent_box0)
solvent_x0 = np.concatenate([min_solvent_coords, mol_coords])
suffix = f"{mol_name}_{epoch}"
seed = np.random.randint(np.iinfo(np.int32).max)
# Order of these simulations should match the order in which predictions are computed to ensure
# efficient use of parallelism.
return {
"solvent_conversion":
binding_model_solvent_conversion.simulate_futures(
ordered_params,
mol,
solvent_x0,
solvent_box0,
prefix="solvent_conversion_" + suffix,
seed=seed,
),
"solvent_decouple":
binding_model_solvent_decouple.simulate_futures(
ordered_params,
mol,
solvent_x0,
solvent_box0,
prefix="solvent_decouple_" + suffix,
seed=seed,
),
"complex_conversion":
binding_model_complex_conversion.simulate_futures(
ordered_params,
mol,
complex_conversion_x0,
complex_box0,
prefix="complex_conversion_" + suffix,
seed=seed,
),
"complex_decouple":
binding_model_complex_decouple.simulate_futures(
ordered_params,
mol,
blocker_mol,
core_idxs,
complex_decouple_x0,
complex_box0,
prefix="complex_decouple_" + suffix,
seed=seed,
),
"mol":
mol,
"blocker":
blocker,
"epoch":
epoch,
"seed":
seed,
}