def minimise_energy_all_confs(mol, models = None, epsilon = 4, allow_undefined_stereo = True, **kwargs ):
from simtk import unit
from simtk.openmm import LangevinIntegrator
from simtk.openmm.app import Simulation, HBonds, NoCutoff
from rdkit import Chem
from rdkit.Geometry import Point3D
import mlddec
import copy
import tqdm
mol = Chem.AddHs(mol, addCoords = True)
if models is None:
models = mlddec.load_models(epsilon)
charges = mlddec.get_charges(mol, models)
from openforcefield.utils.toolkits import RDKitToolkitWrapper, ToolkitRegistry
from openforcefield.topology import Molecule, Topology
from openforcefield.typing.engines.smirnoff import ForceField
# from openforcefield.typing.engines.smirnoff.forcefield import PME
import parmed
import numpy as np
forcefield = ForceField(get_data_filename("modified_smirnoff99Frosst.offxml")) #FIXME better way of identifying file location
tmp = copy.deepcopy(mol)
tmp.RemoveAllConformers() #XXX workround for speed beacuse seemingly openforcefield records all conformer informations, which takes a long time. but I think this is a ill-practice
molecule = Molecule.from_rdkit(tmp, allow_undefined_stereo = allow_undefined_stereo)
molecule.partial_charges = unit.Quantity(np.array(charges), unit.elementary_charge)
topology = Topology.from_molecules(molecule)
openmm_system = forcefield.create_openmm_system(topology, charge_from_molecules= [molecule])
structure = parmed.openmm.topsystem.load_topology(topology.to_openmm(), openmm_system)
system = structure.createSystem(nonbondedMethod=NoCutoff, nonbondedCutoff=1*unit.nanometer, constraints=HBonds)
integrator = LangevinIntegrator(273*unit.kelvin, 1/unit.picosecond, 0.002*unit.picoseconds)
simulation = Simulation(structure.topology, system, integrator)
out_mol = copy.deepcopy(mol)
for i in tqdm.tqdm(range(out_mol.GetNumConformers())):
conf = mol.GetConformer(i)
structure.coordinates = unit.Quantity(np.array([np.array(conf.GetAtomPosition(i)) for i in range(mol.GetNumAtoms())]), unit.angstroms)
simulation.context.setPositions(structure.positions)
simulation.minimizeEnergy()
# simulation.step(1)
coords = simulation.context.getState(getPositions = True).getPositions(asNumpy = True).value_in_unit(unit.angstrom)
conf = out_mol.GetConformer(i)
for j in range(out_mol.GetNumAtoms()):
conf.SetAtomPosition(j, Point3D(*coords[j]))
return out_mol
def load_ddec_models(cls, epsilon=4, **kwargs):
"""
Charging molecule using machine learned charge instead of the default AM1-BCC method.
Requires first installing the mlddec(https://github.com/rinikerlab/mlddec) package. Parameters are availible for elements : {H,C,N,O,Cl,Br,F}.
Parameters
------------
epsilon : int
Dielectric constant to be used, polarity of the resulting molecule varies, possible values are {4,78}.
"""
try:
import mlddec
except ImportError:
raise ImportError('mlddec not properly installed')
cls.rf = mlddec.load_models(epsilon)
# cls.charge_engine = cls._ddec_charger
cls._ddec_charger = mlddec.get_charges
from rdkit import Chem
import mlddec
epsilon = 4
models = mlddec.load_models(epsilon)
mol = Chem.MolFromSmiles("N[C@@H](C)CCCC(=O)")
mol = Chem.AddHs(mol)
mlddec.add_charges_to_mol(mol, models)
mlddec.visualize_charges(mol, show_hydrogens=False)