Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2323

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2323

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: CHEMBL2323 (TID: 12162), and it has 111 rows and 62 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.

64 features

pXC50 (target)numeric89 unique values
0 missing
molecule_id (row identifier)nominal111 unique values
0 missing
CATS2D_04_DDnumeric2 unique values
0 missing
SpMAD_EA.ed.numeric87 unique values
0 missing
Eig03_AEA.ed.numeric57 unique values
0 missing
Eig03_EAnumeric61 unique values
0 missing
Eig03_EA.ed.numeric68 unique values
0 missing
SM11_AEA.bo.numeric61 unique values
0 missing
SM12_AEA.dm.numeric68 unique values
0 missing
PW3numeric46 unique values
0 missing
CATS2D_06_DAnumeric7 unique values
0 missing
Eig03_EA.ri.numeric60 unique values
0 missing
Eig03_AEA.ri.numeric63 unique values
0 missing
Eig05_AEA.dm.numeric65 unique values
0 missing
Eig02_AEA.ri.numeric68 unique values
0 missing
Eig02_EAnumeric56 unique values
0 missing
SM10_AEA.bo.numeric56 unique values
0 missing
SM07_EA.ed.numeric83 unique values
0 missing
SM08_EA.ed.numeric84 unique values
0 missing
SM09_EA.ed.numeric82 unique values
0 missing
SM12_AEA.ed.numeric83 unique values
0 missing
SM13_AEA.ed.numeric81 unique values
0 missing
SM14_AEA.ed.numeric83 unique values
0 missing
SM15_AEA.ed.numeric81 unique values
0 missing
GGI2numeric16 unique values
0 missing
Eig03_AEA.bo.numeric48 unique values
0 missing
IC3numeric98 unique values
0 missing
Eig06_EA.dm.numeric27 unique values
0 missing
Eta_betaS_Anumeric42 unique values
0 missing
SM05_EAnumeric46 unique values
0 missing
SM09_EA.ri.numeric95 unique values
0 missing
SM03_EA.ed.numeric59 unique values
0 missing
SM05_AEA.ed.numeric89 unique values
0 missing
SM06_AEA.ed.numeric87 unique values
0 missing
SM03_EA.ri.numeric62 unique values
0 missing
SM05_EA.ri.numeric78 unique values
0 missing
SM07_EA.ri.numeric91 unique values
0 missing
SM08_EA.ri.numeric101 unique values
0 missing
SM10_EA.ri.numeric104 unique values
0 missing
Eig01_EA.bo.numeric56 unique values
0 missing
SM11_AEA.ri.numeric56 unique values
0 missing
SpMax_EA.bo.numeric56 unique values
0 missing
TIC1numeric99 unique values
0 missing
SM10_AEA.ed.numeric87 unique values
0 missing
SM11_AEA.ed.numeric82 unique values
0 missing
SM15_EAnumeric86 unique values
0 missing
SM15_EA.ed.numeric83 unique values
0 missing
SM04_EA.ed.numeric88 unique values
0 missing
SM05_EA.ed.numeric76 unique values
0 missing
SM06_EA.ed.numeric85 unique values
0 missing
SM07_AEA.ed.numeric87 unique values
0 missing
SM07_EAnumeric66 unique values
0 missing
SM08_AEA.ed.numeric89 unique values
0 missing
SM08_EAnumeric89 unique values
0 missing
SM09_AEA.ed.numeric87 unique values
0 missing
SM09_EAnumeric79 unique values
0 missing
SM10_EAnumeric87 unique values
0 missing
SM11_EAnumeric82 unique values
0 missing
SM12_EAnumeric90 unique values
0 missing
SM13_EAnumeric82 unique values
0 missing
SM14_EAnumeric84 unique values
0 missing
SM10_EA.ed.numeric84 unique values
0 missing
SM11_EA.ed.numeric83 unique values
0 missing
SM12_EA.ed.numeric83 unique values
0 missing

62 properties

111
Number of instances (rows) of the dataset.
64
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.
63
Number of numeric attributes.
1
Number of nominal attributes.
0.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.58
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
9.8
Second quartile (Median) of means 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.
0.8
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
1.17
Mean standard deviation of attributes of the numeric type.
0.81
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.81
Minimum kurtosis among attributes of the numeric type.
0.31
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
4.35
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
1.37
Third quartile of kurtosis among attributes of the numeric type.
243.08
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.44
Percentage of numeric attributes.
16.53
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-1.15
Minimum skewness among attributes of the numeric type.
1.56
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.31
Third quartile of skewness among attributes of the numeric type.
1.82
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.69
First quartile of kurtosis among attributes of the numeric type.
0.49
Third quartile of standard deviation of attributes of the numeric type.
48.32
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
3.73
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.37
Mean kurtosis among attributes of the numeric type.
0.42
First quartile of skewness among attributes of the numeric type.
14.71
Mean of means among attributes of the numeric type.
0.25
First quartile of standard deviation of attributes of the numeric type.
-0.09
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among 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.

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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