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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL213

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL213

deactivated ARFF Publicly available Visibility: public Uploaded 14-07-2016 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: CHEMBL213 (TID: 50), and it has 1414 rows and 30 features (not including molecule IDs and class feature: molecule_id and pXC50). The features represent Basic 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.

32 features

pXC50 (target)numeric567 unique values
0 missing
molecule_id (row identifier)nominal1414 unique values
0 missing
nNnumeric10 unique values
0 missing
N.numeric99 unique values
0 missing
Mpnumeric131 unique values
0 missing
H.numeric162 unique values
0 missing
nSnumeric5 unique values
0 missing
nDBnumeric8 unique values
0 missing
nBMnumeric32 unique values
0 missing
O.numeric103 unique values
0 missing
nHetnumeric16 unique values
0 missing
SCBOnumeric110 unique values
0 missing
nABnumeric21 unique values
0 missing
nCsp2numeric31 unique values
0 missing
nBOnumeric49 unique values
0 missing
Minumeric52 unique values
0 missing
nOnumeric10 unique values
0 missing
RBFnumeric179 unique values
0 missing
MWnumeric969 unique values
0 missing
Mvnumeric133 unique values
0 missing
Svnumeric959 unique values
0 missing
nSKnumeric45 unique values
0 missing
AMWnumeric764 unique values
0 missing
nHMnumeric5 unique values
0 missing
nCspnumeric4 unique values
0 missing
nTBnumeric3 unique values
0 missing
nCnumeric38 unique values
0 missing
nCLnumeric5 unique values
0 missing
Spnumeric917 unique values
0 missing
nBTnumeric79 unique values
0 missing
nATnumeric79 unique values
0 missing
Sinumeric973 unique values
0 missing

62 properties

1414
Number of instances (rows) of the dataset.
32
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.
31
Number of numeric attributes.
1
Number of nominal attributes.
Maximum entropy among attributes.
0.13
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
2.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
66.66
Maximum kurtosis among attributes of the numeric type.
0.04
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
469.64
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.
2.57
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.
96.88
Percentage of numeric attributes.
38.6
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-0.87
Minimum skewness among attributes of the numeric type.
3.13
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7.15
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.
0.82
Third quartile of skewness among attributes of the numeric type.
101.98
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.56
First quartile of kurtosis among attributes of the numeric type.
7.5
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.
0.83
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
5.25
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.
33.28
Mean of means among attributes of the numeric type.
0.2
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.67
First quartile of standard deviation of attributes of the numeric type.
0.06
Average class difference between consecutive instances.
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.
0.02
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
1.7
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.
0.84
Mean skewness among attributes of the numeric type.
7.58
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
7.31
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.
0.37
Second quartile (Median) of skewness among attributes of the numeric type.

12 tasks

1 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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