def a2md_from_mol(mol: Mol2):
"""
returns the density model associated to a Mol2
:param mol:
:type mol: Mol2
:return:
:rtype: Molecule
"""
an = mol.get_atomic_numbers()
coords = mol.get_coordinates(units="au")
charge = mol.get_absolute_charges()
topology = mol.get_bonds()
segments = mol.segment_idx
topo_array = []
for i in range(mol.get_number_atoms()):
topo_array.append([])
for i in range(mol.get_number_bonds()):
begin = int(topology[i, 0])
end = int(topology[i, 1])
topo_array[begin - 1].append(end - 1)
topo_array[end - 1].append(begin - 1)
return Molecule(coordinates=coords,
atomic_numbers=an,
charge=charge,
topology=topo_array,
segments=segments)
def rename_atoms(mol: Mol2, new_names: Dict):
n = mol.get_number_atoms()
if len(new_names['_ATOMS']) != n:
raise RuntimeError("number of atoms does not match")
new_anames = [i[0] for i in new_names['_ATOMS']]
new_segmentsidx = [i[1] for i in new_names['_ATOMS']]
new_segments = [new_names['_SEGMENTS'][i[1]] for i in new_names['_ATOMS']]
mol.atom_names = new_anames
mol.segments = new_segments
mol.segment_idx = new_segmentsidx
return mol
def split_space(mm: Mol2, fun: Callable):
"""
split space
---
divides fun in n functions centered around atoms
:param mm:
:param fun:
:return:
"""
r = mm.get_coordinates(units='au')
n = mm.get_number_atoms()
for i in range(n):
r0 = r[i, :]
yield lambda x: fun(x + r0) * (voronoi(x + r0, r) == i)
def integrate_density_functional_gradient(functional: Callable,
mol: Mol2,
nfuns: int,
grid='coarse',
res=100):
r = mol.get_coordinates(units='au')
n = mol.get_number_atoms()
functional_value = np.zeros(nfuns, dtype='float64')
for i in range(n):
r0 = r[i, :]
fx = lambda x: functional(x + r0) * (voronoi(x + r0, r) == i).reshape(
-1, 1)
functional_value += pi_lebedev_m(fx, nfuns, radial_res=res, grid=grid)
return functional_value
def convert(mol : Mol2):
G = nx.Graph()
for i in range(mol.get_number_atoms()):
G.add_node(i, **mol.get_atom(i))
bonds = mol.get_bonds()
bonds = bonds - 1
for i in range(mol.get_number_bonds()):
G.add_edge(bonds[i, 0], bonds[i, 1])
bonds, angles, dihedrals = decompose_molecule_graph(G)
dbonds = np.zeros(len(bonds), dtype='float64')
dangles = np.zeros(len(angles), dtype='float64')
ddihedrals = np.zeros(len(dihedrals), dtype='float64')
for i in range(len(bonds)):
atom1, atom2 = bonds[i].split("|")
atom1 = int(atom1)
atom2 = int(atom2)
x1 = G.nodes[atom1]['coordinates']
x2 = G.nodes[atom2]['coordinates']
dbonds[i] = distance(x1, x2)
for i in range(len(angles)):
ends, center = angles[i].split(",")
atom1, atom3 = ends.split("|")
atom1 = int(atom1)
atom3 = int(atom3)
atom2 = int(center)
x1 = G.nodes[atom1]['coordinates']
x2 = G.nodes[atom2]['coordinates']
x3 = G.nodes[atom3]['coordinates']
dangles[i] = angle(x1, x2, x3)
for i in range(len(dihedrals)):
ends, center = dihedrals[i].split(",")
atom1, atom4 = ends.split("|")
atom2, atom3 = center.split("|")
atom1 = int(atom1)
atom3 = int(atom3)
atom2 = int(atom2)
atom4 = int(atom4)
x1 = G.nodes[atom1]['coordinates']
x2 = G.nodes[atom2]['coordinates']
x3 = G.nodes[atom3]['coordinates']
x4 = G.nodes[atom4]['coordinates']
ddihedrals[i] = dihedral(x1, x2, x3, x4)
return dbonds, dangles, ddihedrals
def genamd_from_mol2(mol: Mol2, device: torch.device, nfuns: int):
labels = mol.get_symbols()
centers = torch.tensor(mol.get_coordinates(units='au'),
dtype=torch.float,
device=device)
labels = torch.tensor([SYMBOL2NN[i] for i in labels],
dtype=torch.long,
device=device)
centers.unsqueeze_(0)
labels.unsqueeze_(0)
natoms = mol.get_number_atoms()
coefficients = torch.ones(1,
natoms,
nfuns,
dtype=torch.float,
device=device)
return labels, centers, coefficients