GUSAR software was developed to create QSAR/QSPR models on the basis of the appropriate training sets represented as SDfile contained data about chemical structures and endpoint in quantitative terms.
PREDICTION OF ANTITARGETS INTERACTION PROFILES
Quantitative prediction of antitarget interaction profiles for chemical compounds by GUSAR software. The QSAR models for the sets of thirty two end-points (IC50, Ki and Kact) include the data about 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes and 3 transporters).
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DRAW STRUCTURE
Activity Name
End- point
Number of compounds
Training set / Test set
Number of models
R2 training set
Q2 training set
R2 test set
Coverage,%
5-hydroxytryptamine 1B receptor antagonist
IC50
297 / 74
8
0.83
0.79
0.67
100.0
5-hydroxytryptamine 1B receptor antagonist
Ki
266 / 66
7
0.73
0.66
0.72
100.0
5-hydroxytryptamine 2A receptor antagonist
IC50
555 / 143
13
0.83
0.78
0.71
98.6
5-hydroxytryptamine 2A receptor antagonist
Ki
1010 / 252
13
0.72
0.65
0.59
99.6
5-hydroxytryptamine 2C receptor antagonist
IC50
128 / 32
18
0.77
0.73
0.58
100.0
5-hydroxytryptamine 2C receptor antagonist
Ki
487 / 121
14
0.74
0.66
0.62
99.2
alpha1a adrenergic receptor antagonist
IC50
438 / 111
16
0.79
0.73
0.72
98.2
alpha1a adrenergic receptor antagonist
Ki
1366 / 344
5
0.83
0.79
0.80
97.0
alpha1b adrenergic receptor antagonist
Ki
410 / 102
17
0.73
0.66
0.63
100.0
alpha-2A adrenergic receptor antagonist
IC50
109 / 27
16
0.88
0.84
0.75
100.0
alpha-2A adrenergic receptor antagonist
Ki
525 / 131
17
0.84
0.79
0.77
99.2
amine oxidase [flavin-containing] A inhibitor
IC50
286 / 71
9
0.80
0.75
0.72
100.0
amine oxidase [flavin-containing] A inhibitor
Ki
60 / 15
5
0.73
0.62
0.64
100.0
androgen receptor antagonist
IC50
116 / 29
8
0.79
0.73
0.67
100.0
carbonic anhydrase II activator
Kact
104 / 26
20
0.92
0.90
0.91
100.0
carbonic anhydrase I activator
Kact
108 / 27
12
0.98
0.97
0.93
100.0
carbonic anhydrase I inhibitor
Ki
935 / 234
11
0.91
0.86
0.86
98.3
carbonic anhydrase II inhibitor
IC50
866 / 217
7
0.87
0.79
0.76
98.6
d(1A) dopamine receptor antagonist
IC50
126 / 31
11
0.76
0.72
0.80
100.0
d(1A) dopamine receptor antagonist
Ki
291 / 73
10
0.72
0.66
0.57
100.0
d3 dopamine receptor antagonist
Ki
822 / 206
9
0.73
0.66
0.62
98.0
delta-type opioid receptor antagonist
Ki
1044 / 261
16
0.75
0.70
0.65
98.5
estrogen receptor antagonist
IC50
402 / 100
4
0.66
0.61
0.70
97.0
estrogen receptor antagonist
Ki
255 / 68
13
0.76
0.71
0.70
100.0
kappa-type opioid receptor antagonist
Ki
884 / 221
7
0.74
0.67
0.65
100.0
mu-type opioid receptor antagonist
IC50
545 / 136
7
0.67
0.61
0.70
97.8
mu-type opioid receptor antagonist
Ki
1354 / 338
4
0.69
0.62
0.60
96.7
sodium- and chloride-dependent GABA transporter 1 antagonist
IC50
75 / 19
10
0.9
0.86
0.89
100.0
sodium-dependent dopamine transporter antagonist
IC50
920 / 230
5
0.7
0.65
0.67
98.3
sodium-dependent dopamine transporter antagonist
Ki
655 / 164
7
0.77
0.69
0.64
100.0
sodium-dependent serotonin transporter antagonist
IC50
796 / 199
7
0.8
0.75
0.69
97.5
sodium-dependent serotonin transporter antagonist
Ki
823 / 206
2
0.72
0.65
0.61
95.6
Please cite us: Zakharov A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. Quantitative prediction of antitarget interaction profiles for chemical compounds. Chemical Research in Toxicology, 2012, 25(11), 2378-2385 (doi: 10.1021/tx300247r)
CHARACTERISTICS OF QSAR MODELS FOR ANTITARGETS SETS