def _remap_single_result(
self,
mapping: Dict[int, int],
new_molecule: off.Molecule,
result: SingleResult,
extras: Optional[Dict[str, Any]] = None,
) -> SingleResult:
"""
Given a single result and a mapping remap the result ordering to match the fitting schema order.
Parameters:
mapping: The mapping between the old and new molecule.
new_molecule: The new molecule in the correct order.
result: The single result which should be remapped.
extras: Any extras that should be added to the result.
"""
new_molecule._conformers = []
# re map the geometry and attach
new_conformer = np.zeros((new_molecule.n_atoms, 3))
for i in range(new_molecule.n_atoms):
new_conformer[mapping[i]] = result.molecule.geometry[i]
geometry = unit.Quantity(new_conformer, unit.bohr)
new_molecule.add_conformer(geometry)
# drop the bond order indices and just remap the gradient and hessian
new_gradient = np.zeros((new_molecule.n_atoms, 3))
for i in range(new_molecule.n_atoms):
new_gradient[i] = result.gradient[mapping[i]]
# #remap the hessian
# new_hessian = np.zeros((3 * new_molecule.n_atoms, 3 * new_molecule.n_atoms))
# # we need to move 3 entries at a time to keep them together
# for i in range(new_molecule.n_atoms):
# new_hessian[i * 3: (i * 3) + 3] = result.hessian[mapping[i * 3]: mapping[(i * 3) + 3]]
return SingleResult(
molecule=new_molecule.to_qcschema(),
id=result.id,
energy=result.energy,
gradient=new_gradient,
hessian=None,
extras=extras,
)
def _topology_molecule_to_mol2(topology_molecule, file_name,
charge_backend):
"""Converts an `openforcefield.topology.TopologyMolecule` into a mol2 file,
generating a conformer and AM1BCC charges in the process.
.. todo :: This function uses non-public methods from the Open Force Field toolkit
and should be refactored when public methods become available
Parameters
----------
topology_molecule: openforcefield.topology.TopologyMolecule
The `TopologyMolecule` to write out as a mol2 file. The atom ordering in
this mol2 will be consistent with the topology ordering.
file_name: str
The filename to write to.
charge_backend: BuildTLeapSystem.ChargeBackend
The backend to use for conformer generation and partial charge
calculation.
"""
from openforcefield.topology import Molecule
from simtk import unit as simtk_unit
# Make a copy of the reference molecule so we can run conf gen / charge calc without modifying the original
reference_molecule = copy.deepcopy(
topology_molecule.reference_molecule)
if charge_backend == BuildTLeapSystem.ChargeBackend.OpenEye:
from openforcefield.utils.toolkits import OpenEyeToolkitWrapper
toolkit_wrapper = OpenEyeToolkitWrapper()
reference_molecule.generate_conformers(
toolkit_registry=toolkit_wrapper)
reference_molecule.compute_partial_charges_am1bcc(
toolkit_registry=toolkit_wrapper)
elif charge_backend == BuildTLeapSystem.ChargeBackend.AmberTools:
from openforcefield.utils.toolkits import RDKitToolkitWrapper, AmberToolsToolkitWrapper, ToolkitRegistry
toolkit_wrapper = ToolkitRegistry(toolkit_precedence=[
RDKitToolkitWrapper, AmberToolsToolkitWrapper
])
reference_molecule.generate_conformers(
toolkit_registry=toolkit_wrapper)
reference_molecule.compute_partial_charges_am1bcc(
toolkit_registry=toolkit_wrapper)
else:
raise ValueError(f'Invalid toolkit specification.')
# Get access to the parent topology, so we can look up the topology atom indices later.
topology = topology_molecule.topology
# Make and populate a new openforcefield.topology.Molecule
new_molecule = Molecule()
new_molecule.name = reference_molecule.name
# Add atoms to the new molecule in the correct order
for topology_atom in topology_molecule.atoms:
# Force the topology to cache the topology molecule start indices
topology.atom(topology_atom.topology_atom_index)
new_molecule.add_atom(topology_atom.atom.atomic_number,
topology_atom.atom.formal_charge,
topology_atom.atom.is_aromatic,
topology_atom.atom.stereochemistry,
topology_atom.atom.name)
# Add bonds to the new molecule
for topology_bond in topology_molecule.bonds:
# This is a temporary workaround to figure out what the "local" atom index of
# these atoms is. In other words it is the offset we need to apply to get the
# index if this were the only molecule in the whole Topology. We need to apply
# this offset because `new_molecule` begins its atom indexing at 0, not the
# real topology atom index (which we do know).
index_offset = topology_molecule._atom_start_topology_index
# Convert the `.atoms` generator into a list so we can access it by index
topology_atoms = list(topology_bond.atoms)
new_molecule.add_bond(
topology_atoms[0].topology_atom_index - index_offset,
topology_atoms[1].topology_atom_index - index_offset,
topology_bond.bond.bond_order,
topology_bond.bond.is_aromatic,
topology_bond.bond.stereochemistry,
)
# Transfer over existing conformers and partial charges, accounting for the
# reference/topology indexing differences
new_conformers = np.zeros((reference_molecule.n_atoms, 3))
new_charges = np.zeros(reference_molecule.n_atoms)
# Then iterate over the reference atoms, mapping their indices to the topology
# molecule's indexing system
for reference_atom_index in range(reference_molecule.n_atoms):
# We don't need to apply the offset here, since _ref_to_top_index is
# already "locally" indexed for this topology molecule
local_top_index = topology_molecule._ref_to_top_index[
reference_atom_index]
new_conformers[local_top_index, :] = reference_molecule.conformers[
0][reference_atom_index].value_in_unit(simtk_unit.angstrom)
new_charges[local_top_index] = reference_molecule.partial_charges[
reference_atom_index].value_in_unit(
simtk_unit.elementary_charge)
# Reattach the units
new_molecule.add_conformer(new_conformers * simtk_unit.angstrom)
new_molecule.partial_charges = new_charges * simtk_unit.elementary_charge
# Write the molecule
new_molecule.to_file(file_name, file_format='mol2')
def compute_conformer_energies_from_file(filename):
# Load in the molecule and its conformers.
# Note that all conformers of the same molecule are loaded as separate Molecule objects
# If using a OFF Toolkit version before 0.7.0, loading SDFs through RDKit and OpenEye may provide
# different behavior in some cases. So, here we force loading through RDKit to ensure the correct behavior
rdktkw = RDKitToolkitWrapper()
loaded_molecules = Molecule.from_file(filename, toolkit_registry=rdktkw)
# Collatate all conformers of the same molecule
# NOTE: This isn't necessary if you have already loaded or created multi-conformer molecules;
# it is just needed because our SDF reader does not automatically collapse conformers.
molecules = [loaded_molecules[0]]
for molecule in loaded_molecules[1:]:
if molecule == molecules[-1]:
for conformer in molecule.conformers:
molecules[-1].add_conformer(conformer)
else:
molecules.append(molecule)
n_molecules = len(molecules)
n_conformers = sum([mol.n_conformers for mol in molecules])
print(
f'{n_molecules} unique molecule(s) loaded, with {n_conformers} total conformers'
)
# Load the openff-1.1.0 force field appropriate for vacuum calculations (without constraints)
from openforcefield.typing.engines.smirnoff import ForceField
forcefield = ForceField('openff_unconstrained-1.1.0.offxml')
# Loop over molecules and minimize each conformer
for molecule in molecules:
# If the molecule doesn't have a name, set mol.name to be the hill formula
if molecule.name == '':
molecule.name = Topology._networkx_to_hill_formula(
molecule.to_networkx())
print('%s : %d conformers' %
(molecule.name, molecule.n_conformers))
# Make a temporary copy of the molecule that we can update for each minimization
mol_copy = Molecule(molecule)
# Make an OpenFF Topology so we can parameterize the system
off_top = molecule.to_topology()
print(
f"Parametrizing {molecule.name} (may take a moment to calculate charges)"
)
system = forcefield.create_openmm_system(off_top)
# Use OpenMM to compute initial and minimized energy for all conformers
integrator = openmm.VerletIntegrator(1 * unit.femtoseconds)
platform = openmm.Platform.getPlatformByName('Reference')
omm_top = off_top.to_openmm()
simulation = openmm.app.Simulation(omm_top, system, integrator,
platform)
# Print text header
print(
'Conformer Initial PE Minimized PE RMS between initial and minimized conformer'
)
output = [[
'Conformer', 'Initial PE (kcal/mol)', 'Minimized PE (kcal/mol)',
'RMS between initial and minimized conformer (Angstrom)'
]]
for conformer_index, conformer in enumerate(molecule.conformers):
simulation.context.setPositions(conformer)
orig_potential = simulation.context.getState(
getEnergy=True).getPotentialEnergy()
simulation.minimizeEnergy()
min_state = simulation.context.getState(getEnergy=True,
getPositions=True)
min_potential = min_state.getPotentialEnergy()
# Calculate the RMSD between the initial and minimized conformer
min_coords = min_state.getPositions()
min_coords = np.array([[atom.x, atom.y, atom.z]
for atom in min_coords]) * unit.nanometer
mol_copy._conformers = None
mol_copy.add_conformer(conformer)
mol_copy.add_conformer(min_coords)
rdmol = mol_copy.to_rdkit()
rmslist = []
rdMolAlign.AlignMolConformers(rdmol, RMSlist=rmslist)
minimization_rms = rmslist[0]
# Save the minimized conformer to file
mol_copy._conformers = None
mol_copy.add_conformer(min_coords)
mol_copy.to_file(
f'{molecule.name}_conf{conformer_index+1}_minimized.sdf',
file_format='sdf')
print(
'%5d / %5d : %8.3f kcal/mol %8.3f kcal/mol %8.3f Angstroms' %
(conformer_index + 1, molecule.n_conformers,
orig_potential / unit.kilocalories_per_mole,
min_potential / unit.kilocalories_per_mole, minimization_rms))
output.append([
str(conformer_index + 1),
f'{orig_potential/unit.kilocalories_per_mole:.3f}',
f'{min_potential/unit.kilocalories_per_mole:.3f}',
f'{minimization_rms:.3f}'
])
# Write the results out to CSV
with open(f'{molecule.name}.csv', 'w') as of:
for line in output:
of.write(','.join(line) + '\n')
# Clean up OpenMM Simulation
del simulation, integrator