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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL230

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL230

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: CHEMBL230 (TID: 126), and it has 2933 rows and 28 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.

30 features

pXC50 (target)numeric1012 unique values
0 missing
molecule_id (row identifier)nominal2933 unique values
0 missing
X.numeric119 unique values
0 missing
nXnumeric9 unique values
0 missing
nCLnumeric6 unique values
0 missing
nABnumeric21 unique values
0 missing
nSnumeric5 unique values
0 missing
nDBnumeric9 unique values
0 missing
Mvnumeric214 unique values
0 missing
nHMnumeric7 unique values
0 missing
nFnumeric9 unique values
0 missing
AMWnumeric1579 unique values
0 missing
H.numeric256 unique values
0 missing
O.numeric155 unique values
0 missing
MWnumeric1880 unique values
0 missing
Mpnumeric207 unique values
0 missing
RBFnumeric191 unique values
0 missing
nOnumeric33 unique values
0 missing
C.numeric170 unique values
0 missing
nCsp2numeric31 unique values
0 missing
nBMnumeric34 unique values
0 missing
nATnumeric90 unique values
0 missing
Svnumeric1896 unique values
0 missing
nSKnumeric58 unique values
0 missing
Menumeric127 unique values
0 missing
nBOnumeric64 unique values
0 missing
Spnumeric1634 unique values
0 missing
nHetnumeric35 unique values
0 missing
SCBOnumeric112 unique values
0 missing
Senumeric1800 unique values
0 missing

62 properties

2933
Number of instances (rows) of the dataset.
30
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.
29
Number of numeric attributes.
1
Number of nominal attributes.
Third quartile of entropy among attributes.
122.49
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.
92.08
Third quartile of kurtosis among attributes of the numeric type.
384.56
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.
30.06
Third quartile of means 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.
96.67
Percentage of numeric 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.
-0.49
Minimum skewness among attributes of the numeric type.
3.33
Percentage of nominal attributes.
8.22
Third quartile of skewness among attributes of the numeric type.
10.01
Maximum skewness among attributes of the numeric type.
0.02
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
10.07
Third quartile of standard deviation of attributes of the numeric type.
137.68
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.72
First quartile of kurtosis 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.
Number of instances belonging to the least frequent class.
1.14
First quartile of means among attributes of the numeric type.
32.76
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.
27.64
Mean of means among attributes of the numeric type.
0.38
First quartile of skewness among attributes of the numeric type.
-0.19
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.05
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.
0.01
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
1.09
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.
3.19
Mean skewness among attributes of the numeric type.
7.29
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
9.67
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.
1.01
Second quartile (Median) of skewness among attributes of the numeric type.
3.72
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.55
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.

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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