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dcacefd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | import torch
import matplotlib
import numpy as np
import scipy.stats as sp_stats
atom_encoder = {'H': 1, 'C': 6, 'N': 7, 'O': 8, 'F': 9, 'P': 15, 'S': 16, 'Cl': 17}
atom_decoder = {v: k for k, v in atom_encoder.items()}
# Bond lengths from http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html
bonds1 = {'H': {'H': 74, 'C': 109, 'N': 101, 'O': 96, 'F': 92, 'P': 144, 'S': 134, 'Cl': 127},
'C': {'H': 109, 'C': 154, 'N': 147, 'O': 143, 'F': 135, 'P': 184, 'S': 182, 'Cl': 177},
'N': {'H': 101, 'C': 147, 'N': 145, 'O': 140, 'F': 136, 'P': 177, 'S': 168, 'Cl': 175},
'O': {'H': 96, 'C': 143, 'N': 140, 'O': 148, 'F': 142, 'P': 163, 'S': 151, 'Cl': 164},
'F': {'H': 92, 'C': 135, 'N': 136, 'O': 142, 'F': 142, 'P': 156, 'S': 158, 'Cl': 166},
'P': {'H': 144, 'C': 184, 'N': 177, 'O': 163, 'F': 156, 'P': 221, 'S': 210, 'Cl': 203},
'S': {'H': 134, 'C': 182, 'N': 168, 'O': 151, 'F': 158, 'P': 210, 'S': 204, 'Cl': 207},
'Cl': {'H': 127, 'C': 177, 'N': 175, 'O': 164, 'F': 166, 'P': 203, 'S': 207, 'Cl': 199}
}
bonds2 = {'H': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'C': {'H': -1, 'C': 134, 'N': 129, 'O': 120, 'F': -1, 'P': -1, 'S': 160, 'Cl': -1},
'N': {'H': -1, 'C': 129, 'N': 125, 'O': 121, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'O': {'H': -1, 'C': 120, 'N': 121, 'O': 121, 'F': -1, 'P': 150, 'S': -1, 'Cl': -1},
'F': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'P': {'H': -1, 'C': -1, 'N': -1, 'O': 150, 'F': -1, 'P': -1, 'S': 186, 'Cl': -1},
'S': {'H': -1, 'C': 160, 'N': -1, 'O': -1, 'F': -1, 'P': 186, 'S': -1, 'Cl': -1},
'Cl': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
}
bonds3 = {'H': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'C': {'H': -1, 'C': 120, 'N': 116, 'O': 113, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'N': {'H': -1, 'C': 116, 'N': 110, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'O': {'H': -1, 'C': 113, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'F': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'P': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'S': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
'Cl': {'H': -1, 'C': -1, 'N': -1, 'O': -1, 'F': -1, 'P': -1, 'S': -1, 'Cl': -1},
}
stdv = {'H': 5, 'C': 1, 'N': 1, 'O': 2, 'F': 3}
margin1, margin2, margin3 = 10, 5, 3
allowed_bonds = {'H': 1, 'C': 4, 'N': 3, 'O': 2, 'F': 1, 'P': 5, 'S': 4, 'Cl': 1}
def normalize_histogram(hist):
hist = np.array(hist)
prob = hist / np.sum(hist)
return prob
def coord2distances(x):
x = x.unsqueeze(2)
x_t = x.transpose(1, 2)
dist = (x - x_t) ** 2
dist = torch.sqrt(torch.sum(dist, 3))
dist = dist.flatten()
return dist
def earth_mover_distance(h1, h2):
p1 = normalize_histogram(h1)
p2 = normalize_histogram(h2)
distance = sp_stats.wasserstein_distance(p1, p2)
return distance
def kl_divergence(p1, p2):
return np.sum(p1 * np.log(p1 / p2))
def kl_divergence_sym(h1, h2):
p1 = normalize_histogram(h1) + 1e-10
p2 = normalize_histogram(h2) + 1e-10
kl = kl_divergence(p1, p2)
kl_flipped = kl_divergence(p2, p1)
return (kl + kl_flipped) / 2.
def js_divergence(h1, h2):
p1 = normalize_histogram(h1) + 1e-10
p2 = normalize_histogram(h2) + 1e-10
M = (p1 + p2) / 2
js = (kl_divergence(p1, M) + kl_divergence(p2, M)) / 2
return js
def get_bond_order(atom1, atom2, distance):
distance = 100 * distance # We change the metric
# margin1, margin2 and margin3 have been tuned to maximize the stability of the QM9 true samples
if distance < bonds1[atom1][atom2] + margin1:
thr_bond2 = bonds2[atom1][atom2] + margin2
if distance < thr_bond2:
thr_bond3 = bonds3[atom1][atom2] + margin3
if distance < thr_bond3:
return 3
return 2
return 1
return 0
def check_stability(positions, atom_type, debug=False, hs=False, return_nr_bonds=False):
assert len(positions.shape) == 2
assert positions.shape[1] == 3
x = positions[:, 0]
y = positions[:, 1]
z = positions[:, 2]
nr_bonds = np.zeros(len(x), dtype='int')
for i in range(len(x)):
for j in range(i + 1, len(x)):
p1 = np.array([x[i], y[i], z[i]])
p2 = np.array([x[j], y[j], z[j]])
dist = np.sqrt(np.sum((p1 - p2) ** 2))
atom1, atom2 = atom_decoder[atom_type[i]], atom_decoder[
atom_type[j]]
order = get_bond_order(atom1, atom2, dist)
# if i == 0:
# print(j, order)
nr_bonds[i] += order
nr_bonds[j] += order
nr_stable_bonds = 0
for atom_type_i, nr_bonds_i in zip(atom_type, nr_bonds):
if hs:
is_stable = allowed_bonds[atom_decoder[atom_type_i]] == nr_bonds_i
else:
is_stable = (allowed_bonds[atom_decoder[atom_type_i]] >= nr_bonds_i > 0)
if is_stable == False and debug:
print("Invalid bonds for molecule %s with %d bonds" % (atom_decoder[atom_type_i], nr_bonds_i))
nr_stable_bonds += int(is_stable)
molecule_stable = nr_stable_bonds == len(x)
if return_nr_bonds:
return molecule_stable, nr_stable_bonds, len(x), nr_bonds
else:
return molecule_stable, nr_stable_bonds, len(x)
def analyze_stability_for_molecules(molecule_list):
n_samples = len(molecule_list)
molecule_stable_list = []
molecule_stable = 0
nr_stable_bonds = 0
n_atoms = 0
for one_hot, x in molecule_list:
atom_type = one_hot.argmax(2).squeeze(0).cpu().detach().numpy()
x = x.squeeze(0).cpu().detach().numpy()
validity_results = check_stability(x, atom_type)
molecule_stable += int(validity_results[0])
nr_stable_bonds += int(validity_results[1])
n_atoms += int(validity_results[2])
if validity_results[0]:
molecule_stable_list.append((x, atom_type))
# Validity
fraction_mol_stable = molecule_stable / float(n_samples)
fraction_atm_stable = nr_stable_bonds / float(n_atoms)
validity_dict = {
'mol_stable': fraction_mol_stable,
'atm_stable': fraction_atm_stable,
}
# print('Validity:', validity_dict)
return validity_dict, molecule_stable_list
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