Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2581

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2581

deactivated ARFF Publicly available Visibility: public Uploaded 15-07-2016 by Noureddin Sadawi
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
This dataset contains QSAR data (from ChEMBL version 17) showing activity values (unit is pseudo-pCI50) of several compounds on drug target ChEMBL_ID: CHEMBL2581 (TID: 10003), and it has 1187 rows and 69 features (not including molecule IDs and class feature: molecule_id and pXC50). The features represent Molecular Descriptors which were generated from SMILES strings. Missing value imputation was applied to this dataset (By choosing the Median). Feature selection was also applied.

71 features

pXC50 (target)numeric646 unique values
0 missing
molecule_id (row identifier)nominal1187 unique values
0 missing
PW5numeric56 unique values
0 missing
Eig04_EA.ri.numeric480 unique values
0 missing
CATS2D_03_DDnumeric8 unique values
0 missing
SpMax8_Bh.s.numeric563 unique values
0 missing
ATSC1inumeric651 unique values
0 missing
ATSC1snumeric1048 unique values
0 missing
Eig04_EAnumeric451 unique values
0 missing
SM12_AEA.bo.numeric451 unique values
0 missing
Eig04_AEA.dm.numeric479 unique values
0 missing
C.001numeric13 unique values
0 missing
SsCH3numeric933 unique values
0 missing
nCpnumeric13 unique values
0 missing
X4Anumeric49 unique values
0 missing
CATS2D_06_DLnumeric24 unique values
0 missing
Eig02_AEA.dm.numeric378 unique values
0 missing
Eig08_EA.bo.numeric547 unique values
0 missing
CATS2D_02_DLnumeric14 unique values
0 missing
SpMax2_Bh.s.numeric109 unique values
0 missing
Eig01_AEA.ri.numeric359 unique values
0 missing
SpMax_AEA.ri.numeric359 unique values
0 missing
DLS_05numeric3 unique values
0 missing
P_VSA_LogP_1numeric95 unique values
0 missing
SpDiam_EA.ed.numeric450 unique values
0 missing
Eig01_EA.ed.numeric379 unique values
0 missing
SM10_AEA.dm.numeric379 unique values
0 missing
SpMax_EA.ed.numeric379 unique values
0 missing
X3Anumeric62 unique values
0 missing
Eig01_EAnumeric317 unique values
0 missing
SM09_AEA.bo.numeric317 unique values
0 missing
SpDiam_EAnumeric324 unique values
0 missing
SpMax_EAnumeric317 unique values
0 missing
N.numeric102 unique values
0 missing
X2Avnumeric135 unique values
0 missing
Eig09_EA.bo.numeric510 unique values
0 missing
MCDnumeric293 unique values
0 missing
SM14_EA.ed.numeric642 unique values
0 missing
N.074numeric3 unique values
0 missing
SM13_EA.ed.numeric637 unique values
0 missing
SM12_EA.ed.numeric663 unique values
0 missing
SM11_EA.ed.numeric667 unique values
0 missing
SM10_EA.ed.numeric671 unique values
0 missing
X3Avnumeric93 unique values
0 missing
Infective.80numeric2 unique values
0 missing
BACnumeric217 unique values
0 missing
NdsNnumeric3 unique values
0 missing
Eig06_AEA.bo.numeric514 unique values
0 missing
CATS2D_08_DLnumeric24 unique values
0 missing
N.066numeric2 unique values
0 missing
nRNH2numeric2 unique values
0 missing
X5Avnumeric49 unique values
0 missing
SM03_EA.ri.numeric514 unique values
0 missing
X4Avnumeric69 unique values
0 missing
SpMax1_Bh.v.numeric246 unique values
0 missing
Eig03_AEA.dm.numeric462 unique values
0 missing
PW4numeric83 unique values
0 missing
ATSC2snumeric1094 unique values
0 missing
SpMin3_Bh.v.numeric272 unique values
0 missing
SM14_EA.ri.numeric845 unique values
0 missing
SM15_EA.ri.numeric822 unique values
0 missing
CATS2D_02_APnumeric3 unique values
0 missing
CATS2D_04_PLnumeric5 unique values
0 missing
SpMin3_Bh.p.numeric230 unique values
0 missing
IDEnumeric603 unique values
0 missing
H.046numeric32 unique values
0 missing
nCsp3numeric34 unique values
0 missing
Eig09_AEA.dm.numeric651 unique values
0 missing
Mvnumeric193 unique values
0 missing
LLS_01numeric6 unique values
0 missing
CATS2D_07_PLnumeric6 unique values
0 missing

62 properties

1187
Number of instances (rows) of the dataset.
71
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
70
Number of numeric attributes.
1
Number of nominal attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.06
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.17
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.51
Mean skewness among attributes of the numeric type.
3.38
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
3.59
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.34
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.97
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.42
Second quartile (Median) of standard deviation of attributes of the numeric type.
20.54
Maximum kurtosis among attributes of the numeric type.
0.03
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
85.36
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.88
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
98.59
Percentage of numeric attributes.
7.48
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-1.41
Minimum skewness among attributes of the numeric type.
1.41
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
3.33
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.97
Third quartile of skewness among attributes of the numeric type.
89.21
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.66
First quartile of kurtosis among attributes of the numeric type.
2.03
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.56
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
0.95
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
8.58
Mean of means among attributes of the numeric type.
0.13
First quartile of skewness among attributes of the numeric type.
0.16
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.22
First quartile of standard deviation of attributes of the numeric type.

12 tasks

2 runs - estimation_procedure: Custom 10-fold Crossvalidation - target_feature: pXC50
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task