Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1929

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1929

deactivated ARFF Publicly available Visibility: public Uploaded 15-07-2016 by Noureddin Sadawi
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This dataset contains QSAR data (from ChEMBL version 17) showing activity values (unit is pseudo-pCI50) of several compounds on drug target ChEMBL_ID: CHEMBL1929 (TID: 149), and it has 152 rows and 65 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.

67 features

pXC50 (target)numeric112 unique values
0 missing
molecule_id (row identifier)nominal152 unique values
0 missing
NssCH2numeric10 unique values
0 missing
SpMin1_Bh.i.numeric89 unique values
0 missing
Eig15_AEA.dm.numeric115 unique values
0 missing
Eig14_EA.ed.numeric99 unique values
0 missing
SM09_AEA.ri.numeric99 unique values
0 missing
Eig14_EAnumeric88 unique values
0 missing
Eig14_EA.bo.numeric96 unique values
0 missing
Eig14_EA.ri.numeric105 unique values
0 missing
SM08_AEA.dm.numeric88 unique values
0 missing
Eig15_EA.ed.numeric102 unique values
0 missing
SM10_AEA.ri.numeric102 unique values
0 missing
Eig15_EAnumeric87 unique values
0 missing
Eig15_EA.bo.numeric97 unique values
0 missing
Eig15_EA.ri.numeric109 unique values
0 missing
SM09_AEA.dm.numeric87 unique values
0 missing
SssCH2numeric89 unique values
0 missing
SpMin1_Bh.e.numeric83 unique values
0 missing
Eig14_AEA.dm.numeric115 unique values
0 missing
Hynumeric111 unique values
0 missing
Inflammat.50numeric2 unique values
0 missing
Eig13_EAnumeric91 unique values
0 missing
Eig13_EA.bo.numeric106 unique values
0 missing
Eig13_EA.ed.numeric99 unique values
0 missing
Eig13_EA.ri.numeric116 unique values
0 missing
SM07_AEA.dm.numeric91 unique values
0 missing
SM08_AEA.ri.numeric99 unique values
0 missing
SpMax1_Bh.p.numeric84 unique values
0 missing
Eig13_AEA.dm.numeric126 unique values
0 missing
SpMin7_Bh.i.numeric99 unique values
0 missing
SpMax1_Bh.v.numeric83 unique values
0 missing
ATS5enumeric145 unique values
0 missing
nR10numeric4 unique values
0 missing
LOCnumeric102 unique values
0 missing
SpMax8_Bh.i.numeric132 unique values
0 missing
Chi1_EA.dm.numeric122 unique values
0 missing
ATS3enumeric143 unique values
0 missing
Eig12_EA.bo.numeric119 unique values
0 missing
Eig12_EA.ed.numeric112 unique values
0 missing
SM07_AEA.ri.numeric112 unique values
0 missing
Chi0_EA.dm.numeric120 unique values
0 missing
TPCnumeric115 unique values
0 missing
Wapnumeric122 unique values
0 missing
CATS2D_08_LLnumeric9 unique values
0 missing
ATS4enumeric143 unique values
0 missing
Eig09_AEA.ed.numeric118 unique values
0 missing
Eig12_EAnumeric105 unique values
0 missing
Eig12_EA.ri.numeric125 unique values
0 missing
SM06_AEA.dm.numeric105 unique values
0 missing
ATSC1pnumeric131 unique values
0 missing
ATSC5vnumeric148 unique values
0 missing
CIC0numeric127 unique values
0 missing
ATS1enumeric126 unique values
0 missing
ATS1inumeric123 unique values
0 missing
ATS2enumeric131 unique values
0 missing
nBTnumeric52 unique values
0 missing
Senumeric130 unique values
0 missing
TIC5numeric125 unique values
0 missing
ATS5inumeric143 unique values
0 missing
ECCnumeric107 unique values
0 missing
N.068numeric3 unique values
0 missing
nRNR2numeric3 unique values
0 missing
Chi0_EA.bo.numeric126 unique values
0 missing
IDMTnumeric125 unique values
0 missing
SpMax7_Bh.e.numeric116 unique values
0 missing
ATSC6pnumeric149 unique values
0 missing

62 properties

152
Number of instances (rows) of the dataset.
67
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.
66
Number of numeric attributes.
1
Number of nominal attributes.
18953.86
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.
1.09
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.
98.51
Percentage of numeric attributes.
4.31
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-1.06
Minimum skewness among attributes of the numeric type.
1.49
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
4.34
Maximum skewness among attributes of the numeric type.
0.06
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.17
Third quartile of skewness among attributes of the numeric type.
35452.24
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.26
First quartile of kurtosis among attributes of the numeric type.
2.65
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.
-0.62
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
1.92
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.
504.31
Mean of means among attributes of the numeric type.
0.41
First quartile of skewness among attributes of the numeric type.
0.21
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.6
First quartile of standard deviation of attributes of the numeric type.
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.
0.44
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.12
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.
0.87
Mean skewness among attributes of the numeric type.
1.2
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
852.09
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.
0.66
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.87
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
1.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
20.39
Maximum kurtosis among attributes of the numeric type.
-2.25
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.

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
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