Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3070

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3070

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: CHEMBL3070 (TID: 11088), and it has 155 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)numeric104 unique values
0 missing
molecule_id (row identifier)nominal155 unique values
0 missing
MATS6mnumeric131 unique values
0 missing
GATS6mnumeric133 unique values
0 missing
X5Avnumeric27 unique values
0 missing
CATS2D_06_DAnumeric6 unique values
0 missing
GATS6snumeric140 unique values
0 missing
nCsp2numeric16 unique values
0 missing
SpMin2_Bh.e.numeric59 unique values
0 missing
SpMin2_Bh.i.numeric61 unique values
0 missing
BLInumeric113 unique values
0 missing
X1Avnumeric74 unique values
0 missing
GATS6enumeric145 unique values
0 missing
Eig02_AEA.ri.numeric116 unique values
0 missing
MATS6snumeric128 unique values
0 missing
CATS2D_03_AAnumeric4 unique values
0 missing
Eig08_EA.ri.numeric65 unique values
0 missing
MATS4enumeric132 unique values
0 missing
GGI10numeric56 unique values
0 missing
TPCnumeric75 unique values
0 missing
Wapnumeric92 unique values
0 missing
Yindexnumeric90 unique values
0 missing
ATSC6enumeric137 unique values
0 missing
SM02_EA.ri.numeric125 unique values
0 missing
X2Avnumeric71 unique values
0 missing
X0Avnumeric93 unique values
0 missing
Eig08_EAnumeric56 unique values
0 missing
Eig09_AEA.ri.numeric94 unique values
0 missing
Eig09_EA.ed.numeric67 unique values
0 missing
Eig09_EA.ri.numeric89 unique values
0 missing
Eta_FLnumeric146 unique values
0 missing
SM02_AEA.dm.numeric56 unique values
0 missing
SM04_AEA.ri.numeric67 unique values
0 missing
MATS2mnumeric110 unique values
0 missing
Eig08_AEA.ri.numeric69 unique values
0 missing
Eta_betaPnumeric24 unique values
0 missing
Uinumeric14 unique values
0 missing
Chi0_EA.ri.numeric145 unique values
0 missing
Eig12_EA.ri.numeric127 unique values
0 missing
MATS6enumeric138 unique values
0 missing
ATS8mnumeric144 unique values
0 missing
ATS7mnumeric146 unique values
0 missing
Eig11_EA.ri.numeric113 unique values
0 missing
SpAD_EA.ri.numeric151 unique values
0 missing
SMTIVnumeric139 unique values
0 missing
Eta_betanumeric65 unique values
0 missing
P_VSA_LogP_5numeric71 unique values
0 missing
Chi1_AEA.bo.numeric99 unique values
0 missing
Chi1_AEA.dm.numeric99 unique values
0 missing
Chi1_AEA.ed.numeric99 unique values
0 missing
Chi1_AEA.ri.numeric99 unique values
0 missing
Chi1_EAnumeric99 unique values
0 missing
Eig08_AEA.bo.numeric55 unique values
0 missing
Eig08_EA.bo.numeric57 unique values
0 missing
Eig08_EA.ed.numeric74 unique values
0 missing
Eig09_AEA.bo.numeric60 unique values
0 missing
Eig09_EAnumeric55 unique values
0 missing
Eig09_EA.bo.numeric60 unique values
0 missing
Eig15_AEA.bo.numeric77 unique values
0 missing
piPC01numeric37 unique values
0 missing
piPC03numeric75 unique values
0 missing
SCBOnumeric37 unique values
0 missing
SM03_AEA.dm.numeric55 unique values
0 missing
SM03_AEA.ri.numeric74 unique values
0 missing
GATS6pnumeric140 unique values
0 missing
SpMax7_Bh.s.numeric99 unique values
0 missing
Eig06_AEA.ri.numeric105 unique values
0 missing

62 properties

155
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.
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.
0.43
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
3
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.
0.01
Mean skewness among attributes of the numeric type.
1.9
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
534.31
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.
0.29
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.79
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
64.61
Maximum kurtosis among attributes of the numeric type.
-0.1
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
15363.49
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.
5.82
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.
6.29
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-2.5
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.
7.4
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.
1.06
Third quartile of skewness among attributes of the numeric type.
25191.39
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
1.33
First quartile of kurtosis among attributes of the numeric type.
1.21
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.
0.77
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
4.57
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.
431.04
Mean of means among attributes of the numeric type.
-1.32
First quartile of skewness among attributes of the numeric type.
-0.12
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.21
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
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