def smi2cm(m):
m1 = Chem.MolFromSmiles(m)
m = Chem.AddHs(m1)
AllChem.EmbedMolecule(m,AllChem.ETKDG())
n_atoms = m.GetNumAtoms()
m1=Chem.MolToMolBlock(m)
m1=m1.split()
axis=[]
atom_list=[]
for i in range(0,n_atoms):
axis.append([float(m1[13+16*i]),float(m1[14+i*16]),float(m1[15+16*i])])
atom_list.append(m1[16+16*i])
feat=CoulombMatrix()
mole=(atom_list,axis)
feat.fit([mole])
t=feat.transform([mole])[0]
CM=t.reshape((n_atoms,n_atoms)).tolist()
return CM
])
H2 = (H2_ELES, H2_COORDS)
H2_FULL = (H2_ELES, H2_COORDS, H2_UNIT)
HCN_ELES = ['H', 'C', 'N']
HCN_COORDS = [
[-1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
]
HCN = (HCN_ELES, HCN_COORDS)
if __name__ == "__main__":
# Example of fitting the Coulomb matrix and then saving it
feat = CoulombMatrix()
feat.fit([H2, HCN])
print("Saving Model")
feat.save_json("coulomb_model.json")
print("Loading Model")
feat2 = load_json("coulomb_model.json")
print(feat2.transform([H2, HCN]))
# Example of fitting a generallized crystal with the Coulomb matrix and
# then saving it
input_type = ("elements", "coords", "unit_cell")
radius = 4.1
feat = CoulombMatrix(input_type=input_type)
crystal = GenerallizedCrystal(transformer=feat, radius=radius)
feat.fit([H2_FULL])
for i in range(0,molsum):
atom_nums=int(text[index])
a=text[index+2:index+atom_nums+2]
name='mol'+ str(mol_no)
xyz_dict[name]=a
index=index+atom_nums+2
mol_no=mol_no-1
atom_list=[]
axis=[]
for j in range(0,len(a)):
atom=a[j].split()
atom_list.append(atom[0])
axis.append([float(atom[1]),float(atom[2]),float(atom[3])])
feat=CoulombMatrix()
mole=(atom_list,axis)
feat.fit([mole])
CM=feat.transform([mole])[0]
t=CM.reshape((atom_nums,atom_nums)).tolist()
CM_dict.append(t)
fd.close()
sio.savemat('CM.mat',{'CM':CM_dict})
else:
#smi to coordination and coulomb matrix
filepath=input("what's sdf filename?")
#get CM
def smi2cm(m):
m1 = Chem.MolFromSmiles(m)
m = Chem.AddHs(m1)
AllChem.EmbedMolecule(m,AllChem.ETKDG())
0: {1: '1'},
1: {0: '1'},
}
H2_UNIT = numpy.array([
[2., .5, 0.],
[.25, 1., 0.],
[0., .3, 1.],
])
radius = 4.1
input_type = ("elements", "coords", "unit_cell")
X = (H2_ELES, H2_COORDS, H2_UNIT)
if __name__ == "__main__":
trans = EwaldSumMatrix(input_type=input_type, G_max=3.2, L_max=2.1)
res = trans.fit_transform([X])
print(res)
trans = SineMatrix(input_type=input_type)
res = trans.fit_transform([X])
print(res)
# Example of generallized crystal
# Any transformer can be used as it just expands the molecule using the
# unit cell and coordinates.
cm = CoulombMatrix(input_type=input_type)
trans = GenerallizedCrystal(transformer=cm,
radius=radius)
res = trans.fit_transform([X])
print(res)
from utils import load_qm7
if __name__ == "__main__":
# This is just boiler plate code to load the data
Xin_train, Xin_test, y_train, y_test = load_qm7()
# Change this to make the tranformations parallel
# Values less than 1 will set to the number of cores the CPU has
N_JOBS = 1
# Just a few examples of different features
tfs = [
EncodedBond(n_jobs=N_JOBS),
EncodedBond(spacing="inverse", n_jobs=N_JOBS),
BagOfBonds(n_jobs=N_JOBS),
CoulombMatrix(n_jobs=N_JOBS),
Connectivity(depth=1, n_jobs=N_JOBS),
Connectivity(depth=2, use_bond_order=True, n_jobs=N_JOBS),
Connectivity(depth=3, use_coordination=True, n_jobs=N_JOBS),
]
for tf in tfs:
print(tf)
X_train = tf.fit_transform(Xin_train)
X_test = tf.transform(Xin_test)
# We will not do a hyperparmeter search for simplicity
clf = Ridge()
clf.fit(X_train, y_train)
train_error = MAE(clf.predict(X_train), y_train)
test_error = MAE(clf.predict(X_test), y_test)
HCN_COORDS = [
[-1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
]
HCN_CONNS = {
0: {1: '1'},
1: {0: '1', 2: '3'},
2: {1: '3'},
}
if __name__ == "__main__":
# Example of generating the Coulomb matrix with just elements and coords
# for a single example molecule.
feat = CoulombMatrix()
H2 = (H2_ELES, H2_COORDS)
feat.fit([H2])
print("Transformed H2")
print(feat.transform([H2]))
print()
# Example of generating the Coulomb matrix with just elements and coords
# for multiple molecules.
feat = CoulombMatrix()
HCN = (HCN_ELES, HCN_COORDS)
feat.fit([H2, HCN])
print("Transformed H2")
print(feat.transform([H2]))
print("H2 and HCN transformed")
print(feat.transform([H2, HCN]))
[0., .3, 1.],
])
H2 = (H2_ELES, H2_COORDS)
H2_FULL = (H2_ELES, H2_COORDS, H2_UNIT)
HCN_ELES = ['H', 'C', 'N']
HCN_COORDS = [
[-1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
]
HCN = (HCN_ELES, HCN_COORDS)
if __name__ == "__main__":
# Example of fitting the Coulomb matrix and then saving it
feat = CoulombMatrix()
feat.fit([H2, HCN])
print("Saving Model")
feat.save_json("coulomb_model.json")
print("Loading Model")
feat2 = load_json("coulomb_model.json")
print(feat2.transform([H2, HCN]))
# Example of fitting a generallized crystal with the Coulomb matrix and
# then saving it
input_type = ("elements", "coords", "unit_cell")
radius = 4.1
feat = CoulombMatrix(input_type=input_type)
crystal = GenerallizedCrystal(transformer=feat, radius=radius)
feat.fit([H2_FULL])
from molml.features import CoulombMatrix
feat = CoulombMatrix(input_type='list',
n_jobs=1,
sort=False,
eigen=False,
drop_values=False,
only_lower_triangle=False)
H2 = (['H', 'H'], [
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
HCN = (['H', 'C', 'N'], [
[-1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
])
feat.fit([H2, HCN])
print(feat.transform([H2]))
print(feat.transform([H2, HCN]))
feat2 = CoulombMatrix(input_type='filename')
paths = ['data/qm7/qm-%04d.out' % i for i in range(2)]
print(feat2.fit_transform(paths))