-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathDescriptors_MD.py
170 lines (152 loc) · 5.41 KB
/
Descriptors_MD.py
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
from radonpy.core import poly, utils
from radonpy.ff.gaff2_mod import GAFF2_mod
from radonpy.ff.descriptor import FF_descriptor
import numpy as np
import pandas as pd
import sys
def ff_descriptor(smiles):
mol= poly.make_cyclicpolymer(smiles, n=2, return_mol=True)
ff = GAFF2_mod()
result = ff.ff_assign(mol, charge='gasteiger')
desc=FF_descriptor(ff)
all_desc=desc.get_param_list(mol)
mass_max=np.max(all_desc[0])
mass_min=np.min(all_desc[0])
mass_ave=np.average(all_desc[0])
charge_max=np.max(all_desc[1])
charge_min=np.min(all_desc[1])
charge_ave=np.average(all_desc[1])
epsilon_max=np.max(all_desc[2])
epsilon_min=np.min(all_desc[2])
epsilon_ave=np.average(all_desc[2])
sigma_max=np.max(all_desc[3])
sigma_min=np.min(all_desc[3])
sigma_ave=np.average(all_desc[3])
k_bond_max=np.max(all_desc[4])
k_bond_min=np.min(all_desc[4])
k_bond_ave=np.average(all_desc[4])
r0_max=np.max(all_desc[5])
r0_min=np.min(all_desc[5])
r0_ave=np.average(all_desc[5])
k_ang_max=np.max(all_desc[6])
k_ang_min=np.min(all_desc[6])
k_ang_ave=np.average(all_desc[6])
theta0_max=np.max(all_desc[7])
theta0_min=np.min(all_desc[7])
theta0_ave=np.average(all_desc[7])
k_dih_max=np.max(all_desc[8])
k_dih_min=np.min(all_desc[8])
k_dih_ave=np.average(all_desc[8])
return mass_max,mass_min,mass_ave,charge_max,charge_min,charge_ave,epsilon_max,epsilon_min,epsilon_ave,sigma_max,sigma_min,sigma_ave,\
k_bond_max,k_bond_min,k_bond_ave,r0_max,r0_min, r0_ave, k_ang_max,k_ang_min, k_ang_ave, theta0_max,theta0_min,theta0_ave,k_dih_max,k_dih_min,k_dih_ave
if __name__ == '__main__':
#path="./dataset/"
path="./dataset/Descriptor_data/"
filename=sys.argv[1]
dataframe = pd.read_csv(path+filename)
SMILES=dataframe['SMILES']
ID=dataframe['ID']
Mass_max=[]
Mass_min=[]
Mass_ave=[]
Charge_max=[]
Charge_min=[]
Charge_ave=[]
Epsilon_max=[]
Epsilon_min=[]
Epsilon_ave=[]
Sigma_max=[]
Sigma_min=[]
Sigma_ave=[]
K_bond_max=[]
K_bond_min=[]
K_bond_ave=[]
R0_max=[]
R0_min=[]
R0_ave=[]
K_ang_max=[]
K_ang_min=[]
K_ang_ave=[]
Theta0_max=[]
Theta0_min=[]
Theta0_ave=[]
K_dih_max=[]
K_dih_min=[]
K_dih_ave=[]
NAME=[]
SMILE=[]
success=[]
for i in range(0,len(dataframe)):
smi=SMILES[i]
id_=ID[i]
try:
mass_max,mass_min,mass_ave,charge_max,charge_min,charge_ave,epsilon_max,epsilon_min,epsilon_ave,sigma_max,sigma_min,sigma_ave,\
k_bond_max,k_bond_min,k_bond_ave,r0_max,r0_min, r0_ave, k_ang_max,k_ang_min, k_ang_ave, theta0_max,theta0_min,theta0_ave,\
k_dih_max,k_dih_min,k_dih_ave=ff_descriptor(smi)
NAME.append (id_)
SMILE.append(smi)
Mass_max.append(mass_max)
Mass_min.append(mass_min)
Mass_ave.append(mass_ave)
Charge_max.append(charge_max)
Charge_min.append(charge_min)
Charge_ave.append(charge_ave)
Epsilon_max.append(epsilon_max)
Epsilon_min.append(epsilon_min)
Epsilon_ave.append(epsilon_ave)
Sigma_max.append(sigma_max)
Sigma_min.append(sigma_min)
Sigma_ave.append(sigma_ave)
K_bond_max.append(k_bond_max)
K_bond_min.append(k_bond_min)
K_bond_ave.append(k_bond_ave)
R0_max.append(r0_max)
R0_min.append(r0_min)
R0_ave.append(r0_ave)
K_ang_max.append(k_ang_max)
K_ang_min.append(k_ang_min)
K_ang_ave.append(k_ang_ave)
Theta0_max.append(theta0_max)
Theta0_min.append(theta0_min)
Theta0_ave.append(theta0_ave)
K_dih_max.append(k_dih_max)
K_dih_min.append(k_dih_min)
K_dih_ave.append(k_dih_ave)
success.append ("success")
except:
success.append ("fail")
pass
a = [x for x in Mass_max]
b = [x for x in Mass_min]
c = [x for x in Mass_ave]
d = [x for x in Charge_max]
e = [x for x in Charge_min]
f = [x for x in Charge_ave]
g = [x for x in Epsilon_max]
h = [x for x in Epsilon_min]
i = [x for x in Epsilon_ave]
j = [x for x in Sigma_max]
k = [x for x in Sigma_min]
l = [x for x in Sigma_ave]
m = [x for x in K_bond_max]
n = [x for x in K_bond_min]
o = [x for x in K_bond_ave]
p = [x for x in R0_max]
q = [x for x in R0_min]
r = [x for x in R0_ave]
s = [x for x in K_ang_max]
t = [x for x in K_ang_min]
u = [x for x in K_ang_ave]
v = [x for x in Theta0_max]
w = [x for x in Theta0_min]
x = [x for x in Theta0_ave]
y = [x for x in K_dih_max]
z = [x for x in K_dih_min]
aa = [x for x in K_dih_ave]
name=[x for x in NAME]
smiles=[x for x in SMILE]
dataframe = pd.DataFrame({'ID':name,'Smiles':smiles,'Mass_max':a,'Mass_min':b,'Mass_ave':c,'Charge_max':d,'Charge_min':e,\
'Charge_ave':f,'Epsilon_max':g,'Epsilon_min':h,'Epsilon_ave':i,'Sigma_max':j,'Sigma_min':k,'Sigma_ave':l,'K_bond_max':m,'K_bond_min':n,'K_bond_ave':o,\
'R0_max':p,'R0_min':q,'R0_ave':r,'K_ang_max':s,'K_ang_min':t,'K_ang_ave':u,'Theta0_max':v,'Theta0_min':w,'Theta0_ave':x,'K_dih_max':y,\
'K_dih_min':z,'K_dih_ave':aa})
dataframe.to_csv("./dataset/Descriptors/VII_Descriptors_MD.csv")