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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3769

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3769

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: CHEMBL3769 (TID: 10443), and it has 769 rows and 22 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.

24 features

pXC50 (target)numeric421 unique values
0 missing
molecule_id (row identifier)nominal769 unique values
0 missing
nCsp3numeric30 unique values
0 missing
nHnumeric53 unique values
0 missing
nNnumeric14 unique values
0 missing
AMWnumeric486 unique values
0 missing
X.numeric45 unique values
0 missing
Senumeric561 unique values
0 missing
nATnumeric85 unique values
0 missing
SCBOnumeric93 unique values
0 missing
nXnumeric6 unique values
0 missing
RBNnumeric24 unique values
0 missing
H.numeric178 unique values
0 missing
Mvnumeric144 unique values
0 missing
Sinumeric551 unique values
0 missing
nBTnumeric88 unique values
0 missing
nDBnumeric15 unique values
0 missing
nBOnumeric54 unique values
0 missing
Spnumeric542 unique values
0 missing
nCsp2numeric32 unique values
0 missing
nSKnumeric51 unique values
0 missing
Svnumeric549 unique values
0 missing
nFnumeric5 unique values
0 missing
nCnumeric42 unique values
0 missing

62 properties

769
Number of instances (rows) of the dataset.
24
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.
23
Number of numeric attributes.
1
Number of nominal 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.
Second quartile (Median) of entropy among attributes.
0.03
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
20.99
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.
2.53
Mean skewness among attributes of the numeric type.
25.48
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
8
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.
2.61
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
0.15
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
5.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
59.28
Maximum kurtosis among attributes of the numeric type.
0.3
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
73.57
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.
33.23
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.
95.83
Percentage of numeric attributes.
45.57
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-0.6
Minimum skewness among attributes of the numeric type.
4.17
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
5.53
Maximum skewness among attributes of the numeric type.
0.03
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
3.39
Third quartile of skewness among attributes of the numeric type.
22.54
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
6.3
First quartile of kurtosis among attributes of the numeric type.
12.05
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.
4.97
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
21.95
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.
2.35
First quartile of skewness among attributes of the numeric type.
-0.16
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.67
First quartile of standard deviation of 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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