def _collect_results(self, td_state: Dict[str, Any],
molecule: "Ligand") -> "Ligand":
"""
After the torsiondrive has been completed collect the results and pack them into the ligand.
"""
# torsiondrive will only give us the lowest energy for each grid point
# we have to then work out what the final geometry is from this
optimised_energies = td_api.collect_lowest_energies(td_state=td_state)
final_grid = td_state["grid_status"]
# make a torsiondrive data store assuming 1D only
torsion_data = TorsionDriveData(
grid_spacing=self.grid_spacing,
dihedral=td_state["dihedrals"][0],
torsion_drive_range=td_state["dihedral_ranges"][0],
)
# now grab each grid point in sorted order
for (
angle,
energy,
) in sorted(optimised_energies.items()):
# grab all optimisation done at this angle
optimisations = final_grid[str(angle[0])]
# loop over each result and check if the energy matches the lowest
# results -> (initial geometry, final geometry, final energy)
for result in optimisations:
if result[-1] == energy:
grid_data = TorsionData(
angle=angle[0],
geometry=np.array(result[1]) * constants.BOHR_TO_ANGS,
energy=energy,
)
torsion_data.add_grid_point(grid_data=grid_data)
break
# validate the data
torsion_data.validate_angles()
# dump to file (qdata.txt and scan.xyz)
export_torsiondrive_data(molecule=molecule, tdrive_data=torsion_data)
# dump the qubekit torsion data to file
torsion_data.to_file("scan_data.json")
# save to mol
molecule.add_qm_scan(scan_data=torsion_data)
# dump the torsiondrive state to file
self._dump_state(td_state=td_state)
# now remove all temp folders
for f in os.listdir("."):
if os.path.isdir(f):
shutil.rmtree(f, ignore_errors=True)
return molecule
def prep_for_fitting(self, molecule: Ligand) -> List[str]:
"""
For the given ligand prep the input files ready for torsion profile fitting.
Args:
molecule: The molecule object that we need to prep for fitting, this should have qm reference data stored in molecule.qm_scans.
Note:
We assume we are already in the targets folder.
Returns:
A list of target folder names made by this target.
Raises:
MissingReferenceData: If the molecule does not have any torsion drive reference data saved in molecule.qm_scans.
"""
# make sure we have data
if not molecule.qm_scans:
raise MissingReferenceData(
f"Can not prepare a forcebalance fitting target for {molecule.name} as the reference data is missing!"
)
# write out the qdata and other input files for each scan
target_folders = []
# keep track of where we start
base_folder = os.getcwd()
# loop over each scanned bond and make a target folder
for scan in molecule.qm_scans:
task_name = (
f"{self.target_name}_{scan.central_bond[0]}_{scan.central_bond[1]}"
)
target_folders.append(task_name)
make_and_change_into(name=task_name)
# make the pdb topology file
if molecule.has_ub_terms():
molecule._to_ub_pdb(file_name="molecule")
else:
molecule.to_file(file_name="molecule.pdb")
# write the qdata file
export_torsiondrive_data(molecule=molecule, tdrive_data=scan)
# make the metadata
self.make_metadata(torsiondrive_data=scan)
# now move back to the base
os.chdir(base_folder)
return target_folders
def test_qdata_round_trip(tmpdir):
"""
Try and round trip a qdata txt file to a TorsionDriveData object and back.
"""
with tmpdir.as_cwd():
mol = Ligand.from_file(file_name=get_data("biphenyl.sdf"))
td_ref = TorsionDriveData.from_qdata(
dihedral=(6, 10, 11, 8), qdata_file=get_data("biphenyl_qdata.txt"))
# now write to file
export_torsiondrive_data(molecule=mol, tdrive_data=td_ref)
# now make a second object
td_new = TorsionDriveData.from_qdata(dihedral=(6, 10, 11, 8),
qdata_file="qdata.txt")
assert td_ref.dihedral == td_new.dihedral
for angle, ref_result in td_ref.reference_data.items():
new_result = td_new.reference_data[angle]
assert ref_result.angle == new_result.angle
assert ref_result.energy == pytest.approx(new_result.energy)
assert np.allclose(ref_result.geometry.tolist(),
new_result.geometry.tolist())