def generate_exclusion_idxs(mol, scale12, scale13, scale14):
"""
Generate exclusions for a mol based on the all pairs shortest path.
We always take the convention that exclusions for smaller distances
override those of longer distances.
Parameters
----------
mol: Chem.ROMol
romol
scale12: float
bond scales
scale13: float
angle scales
scale14: float
torsions scales
"""
exclusions = {}
g = convert_to_nx(mol)
for path in nx.all_pairs_shortest_path_length(g, cutoff=3):
src = path[0]
for dst, length in path[1].items():
if length == 0:
continue
else:
if length == 1:
scale = scale12
elif length == 2:
scale = scale13
elif length == 3:
scale = scale14
else:
assert 0
exclusions[sort_tuple((src, dst))] = scale
idxs = list(exclusions.keys())
scales = list(exclusions.values())
return np.array(idxs, dtype=np.int32), np.array(scales, dtype=np.float64)
def generate_vd_idxs(mol, smirks):
"""
Generate bonded indices using a valence dict. The indices generated
are assumed to be reversible. i.e. reversing the indices evaluates to
an identical energy. This is not intended to be used for ImproperTorsions.
"""
vd = {}
for p_idx, patt in enumerate(smirks):
matches = match_smirks(mol, patt)
for m in matches:
sorted_m = sort_tuple(m)
vd[sorted_m] = p_idx
bond_idxs = np.array(list(vd.keys()), dtype=np.int32)
param_idxs = np.array(list(vd.values()), dtype=np.int32)
return bond_idxs, param_idxs