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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5282

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5282

deactivated ARFF Publicly available Visibility: public Uploaded 14-07-2016 by unknown
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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: CHEMBL5282 (TID: 20073), and it has 164 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)numeric105 unique values
0 missing
SM13_EA.ed.numeric90 unique values
0 missing
GGI4numeric73 unique values
0 missing
SM12_EA.ed.numeric92 unique values
0 missing
SM11_EA.ed.numeric92 unique values
0 missing
SM12_EA.ri.numeric158 unique values
0 missing
SM11_EA.ri.numeric153 unique values
0 missing
X3Anumeric40 unique values
0 missing
ATS4enumeric141 unique values
0 missing
TWCnumeric90 unique values
0 missing
MWC10numeric86 unique values
0 missing
MWC09numeric90 unique values
0 missing
SM14_AEA.ed.numeric90 unique values
0 missing
SM13_AEA.ed.numeric88 unique values
0 missing
SM12_AEA.ed.numeric90 unique values
0 missing
SM08_EA.ed.numeric92 unique values
0 missing
SM07_EA.ed.numeric91 unique values
0 missing
IC5numeric89 unique values
0 missing
SM14_EA.ed.numeric91 unique values
0 missing
SM15_EA.ed.numeric92 unique values
0 missing
SM14_EAnumeric91 unique values
0 missing
SM15_EAnumeric92 unique values
0 missing
ATS4snumeric149 unique values
0 missing
MPC06numeric59 unique values
0 missing
MPC08numeric56 unique values
0 missing
MPC09numeric56 unique values
0 missing
SpMin1_Bh.p.numeric99 unique values
0 missing
ATSC5pnumeric154 unique values
0 missing
SM10_EA.ri.numeric154 unique values
0 missing
MPC07numeric61 unique values
0 missing
SM05_EA.ed.numeric84 unique values
0 missing
Eta_sh_ynumeric85 unique values
0 missing
ATSC6snumeric157 unique values
0 missing
SM09_EA.ed.numeric90 unique values
0 missing
SpDiam_AEA.ri.numeric129 unique values
0 missing
SpDiam_EA.ri.numeric139 unique values
0 missing
SM15_EA.ri.numeric156 unique values
0 missing
SM13_EA.ri.numeric159 unique values
0 missing
SM14_EA.ri.numeric158 unique values
0 missing
SpMax_EA.ri.numeric137 unique values
0 missing
Eig01_EA.ri.numeric137 unique values
0 missing
CATS2D_04_ALnumeric16 unique values
0 missing
NdssCnumeric4 unique values
0 missing
IDDEnumeric76 unique values
0 missing
SpMax_AEA.ri.numeric130 unique values
0 missing
Eig01_AEA.ri.numeric130 unique values
0 missing
SpDiam_AEA.ed.numeric87 unique values
0 missing
CATS2D_06_LLnumeric15 unique values
0 missing
Inflammat.50numeric2 unique values
0 missing
C.019numeric2 unique values
0 missing
SssOnumeric38 unique values
0 missing
molecule_id (row identifier)nominal164 unique values
0 missing
SpDiam_EA.ed.numeric89 unique values
0 missing
Eig01_AEA.ed.numeric80 unique values
0 missing
Eig01_EA.ed.numeric88 unique values
0 missing
SM10_AEA.dm.numeric88 unique values
0 missing
SpMax_AEA.ed.numeric80 unique values
0 missing
SpMax_EA.ed.numeric88 unique values
0 missing
C.039numeric2 unique values
0 missing
nArCOnumeric2 unique values
0 missing
Eig01_EAnumeric87 unique values
0 missing
SM09_AEA.bo.numeric87 unique values
0 missing
SpDiam_EAnumeric87 unique values
0 missing
SpMax_EAnumeric87 unique values
0 missing
D.Dtr10numeric34 unique values
0 missing
SpMin1_Bh.v.numeric98 unique values
0 missing
IC4numeric90 unique values
0 missing

62 properties

164
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.
First quartile of mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
98.51
Percentage of numeric attributes.
1.49
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.84
First quartile of kurtosis among attributes of the numeric type.
2.99
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
-0.26
First quartile of skewness among attributes of the numeric type.
0.32
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
1.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
5.07
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.04
Second quartile (Median) of skewness among attributes of the numeric type.
0.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
2.46
Third quartile of kurtosis among attributes of the numeric type.
15.54
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.49
Third quartile of skewness among attributes of the numeric type.
1.32
Third quartile of standard deviation of attributes of the numeric type.
0.25
Average class difference between consecutive instances.
9.71
Mean of means among attributes of the numeric type.
Entropy of the target attribute values.
0.41
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
24.46
Maximum kurtosis among attributes of the numeric type.
34.03
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
3.97
Maximum skewness among attributes of the numeric type.
61.18
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
2.31
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Average number of distinct values among the attributes of the nominal type.
0.4
Mean skewness among attributes of the numeric type.
2.3
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.45
Minimum kurtosis among attributes of the numeric type.
0.1
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
-0.93
Minimum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.

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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