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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4599

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4599

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: CHEMBL4599 (TID: 30023), and it has 526 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)numeric155 unique values
0 missing
molecule_id (row identifier)nominal526 unique values
0 missing
nABnumeric25 unique values
0 missing
nHnumeric42 unique values
0 missing
RBNnumeric15 unique values
0 missing
Spnumeric448 unique values
0 missing
Svnumeric462 unique values
0 missing
nSKnumeric37 unique values
0 missing
SCBOnumeric86 unique values
0 missing
nBOnumeric40 unique values
0 missing
Sinumeric458 unique values
0 missing
nBMnumeric27 unique values
0 missing
nCnumeric33 unique values
0 missing
Senumeric455 unique values
0 missing
nBTnumeric72 unique values
0 missing
MWnumeric459 unique values
0 missing
nATnumeric69 unique values
0 missing
nCsp2numeric24 unique values
0 missing
H.numeric156 unique values
0 missing
Mvnumeric145 unique values
0 missing
nCsp3numeric17 unique values
0 missing
AMWnumeric414 unique values
0 missing
RBFnumeric123 unique values
0 missing
Menumeric73 unique values
0 missing
C.numeric131 unique values
0 missing
Mpnumeric123 unique values
0 missing
nNnumeric10 unique values
0 missing
nHetnumeric14 unique values
0 missing
Minumeric65 unique values
0 missing
N.numeric127 unique values
0 missing

62 properties

526
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.
2.76
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.
0.36
Third quartile of skewness among attributes of the numeric type.
97.46
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.41
First quartile of kurtosis among attributes of the numeric type.
9.11
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.
5.3
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
0.96
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.
35.1
Mean of means among attributes of the numeric type.
-0.26
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
1.43
First quartile of standard deviation of attributes of the numeric type.
0.28
Average class difference between consecutive instances.
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.
Entropy of the target attribute values.
Average number of distinct values among the attributes of the nominal type.
-0.1
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.06
Number of attributes divided by the number of instances.
0.23
Mean skewness among attributes of the numeric type.
19.69
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.
Percentage of instances belonging to the most frequent class.
8.89
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.
-0.05
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.55
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
4.76
Second quartile (Median) of standard deviation of attributes of the numeric type.
14.29
Maximum kurtosis among attributes of the numeric type.
0.09
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
402.37
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.
0.42
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.
96.67
Percentage of numeric attributes.
42.17
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.
3.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.

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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