def assign_elf10_am1_bond_orders(
molecule: Molecule, max_confs: int = 800, rms_threshold: float = 1.0
) -> Molecule:
"""Generate ELF10 AM1 WBOs for a molecule.
Parameters
----------
molecule
The molecule to compute WBOs for.
max_confs : int, optional, default=800
Max number of conformers to generate
rms_threshold
The minimum RMS value [Angstrom] at which two conformers are considered redundant
and one is deleted.
Returns
-------
A new molecule which contains ELF10 AM1 Wiberg Bond orders.
"""
molecule = copy.deepcopy(molecule)
# Store the atom map separately in case it gets removed by a TK.
atom_map = molecule.properties.pop("atom_map", None)
# Generate a set of ELF10 conformers.
molecule = _generate_conformers(molecule, max_confs, rms_threshold)
molecule.apply_elf_conformer_selection()
per_conformer_bond_orders = []
for conformer in molecule.conformers:
molecule.assign_fractional_bond_orders("am1-wiberg", use_conformers=[conformer])
per_conformer_bond_orders.append(
[bond.fractional_bond_order for bond in molecule.bonds]
)
bond_orders = [*numpy.mean(per_conformer_bond_orders, axis=0)]
for bond, bond_order in zip(molecule.bonds, bond_orders):
bond.fractional_bond_order = bond_order
# Restore the atom map.
if atom_map is not None:
molecule.properties["atom_map"] = atom_map
return molecule
def label_function(molecule: Molecule) -> Dict[str, torch.Tensor]:
"""Generates a set of train / val / test labels for a given molecule."""
from simtk import unit
# Generate a set of ELF10 conformers.
molecule.generate_conformers(n_conformers=800,
rms_cutoff=0.05 * unit.angstrom)
molecule.apply_elf_conformer_selection()
partial_charges = []
for conformer in molecule.conformers:
molecule.assign_partial_charges("am1-mulliken",
use_conformers=[conformer])
partial_charges.append(
molecule.partial_charges.value_in_unit(unit.elementary_charge))
return {
"am1-charges":
torch.from_numpy(numpy.mean(partial_charges, axis=0)).float()
}
