Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3062

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3062

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: CHEMBL3062 (TID: 10856), and it has 121 rows and 67 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.

69 features

pXC50 (target)numeric78 unique values
0 missing
molecule_id (row identifier)nominal121 unique values
0 missing
piPC09numeric103 unique values
0 missing
SM07_AEA.bo.numeric104 unique values
0 missing
Eig04_EA.ed.numeric90 unique values
0 missing
SM13_AEA.dm.numeric90 unique values
0 missing
CATS2D_04_ALnumeric21 unique values
0 missing
MWC06numeric102 unique values
0 missing
SM04_AEA.ed.numeric103 unique values
0 missing
SM05_AEA.ed.numeric103 unique values
0 missing
SM06_EA.ri.numeric106 unique values
0 missing
SRW10numeric100 unique values
0 missing
X4vnumeric112 unique values
0 missing
X5vnumeric113 unique values
0 missing
MPC06numeric81 unique values
0 missing
MPC05numeric73 unique values
0 missing
MWC08numeric99 unique values
0 missing
MWC09numeric103 unique values
0 missing
MWC10numeric100 unique values
0 missing
TWCnumeric102 unique values
0 missing
Eig05_AEA.ed.numeric83 unique values
0 missing
Eig05_EA.ed.numeric91 unique values
0 missing
SM14_AEA.dm.numeric91 unique values
0 missing
GGI6numeric96 unique values
0 missing
MPC04numeric64 unique values
0 missing
piPC04numeric95 unique values
0 missing
CATS2D_06_ALnumeric21 unique values
0 missing
SM06_AEA.bo.numeric101 unique values
0 missing
MWC07numeric101 unique values
0 missing
SM11_EA.ri.numeric104 unique values
0 missing
MPC07numeric88 unique values
0 missing
MPC08numeric92 unique values
0 missing
SM06_EA.bo.numeric103 unique values
0 missing
GGI3numeric64 unique values
0 missing
GGI4numeric92 unique values
0 missing
MWC05numeric103 unique values
0 missing
SM02_EA.ed.numeric94 unique values
0 missing
SM03_AEA.ed.numeric99 unique values
0 missing
SM03_EAnumeric18 unique values
0 missing
SM04_EAnumeric89 unique values
0 missing
SM04_EA.ri.numeric102 unique values
0 missing
SM05_EA.ri.numeric100 unique values
0 missing
SRW08numeric96 unique values
0 missing
SM08_AEA.bo.numeric102 unique values
0 missing
CATS2D_05_ALnumeric20 unique values
0 missing
Eig04_AEA.ed.numeric86 unique values
0 missing
SM07_EA.ri.numeric102 unique values
0 missing
SM08_EA.ri.numeric108 unique values
0 missing
Eig07_AEA.ed.numeric87 unique values
0 missing
Eig06_EA.ed.numeric91 unique values
0 missing
SM06_EAnumeric101 unique values
0 missing
SM15_AEA.dm.numeric91 unique values
0 missing
X4numeric104 unique values
0 missing
X4solnumeric106 unique values
0 missing
GGI5numeric86 unique values
0 missing
MPC03numeric57 unique values
0 missing
SM03_EA.ri.numeric82 unique values
0 missing
Eig04_EA.ri.numeric100 unique values
0 missing
X5numeric105 unique values
0 missing
X5solnumeric106 unique values
0 missing
SM12_EA.ri.numeric108 unique values
0 missing
SM07_AEA.ed.numeric98 unique values
0 missing
SM06_AEA.ed.numeric100 unique values
0 missing
SM03_EA.bo.numeric58 unique values
0 missing
SM05_AEA.bo.numeric98 unique values
0 missing
SM04_EA.ed.numeric101 unique values
0 missing
SM05_EAnumeric65 unique values
0 missing
MATS3inumeric91 unique values
0 missing
ATS5mnumeric109 unique values
0 missing

62 properties

121
Number of instances (rows) of the dataset.
69
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.
68
Number of numeric attributes.
1
Number of nominal attributes.
-0.48
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.57
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
7.87
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.
0.27
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.
0.94
Mean standard deviation of attributes of the numeric type.
0.3
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.
0.4
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.16
Minimum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
3.76
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.33
Third quartile of kurtosis among attributes of the numeric type.
14.71
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.55
Percentage of numeric attributes.
10.42
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-1.3
Minimum skewness among attributes of the numeric type.
1.45
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.
0.08
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.72
Third quartile of skewness among attributes of the numeric type.
1.52
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.7
First quartile of kurtosis among attributes of the numeric type.
1.06
Third quartile of standard deviation of attributes of the numeric type.
5.09
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
5.17
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.
0.08
Mean kurtosis among attributes of the numeric type.
0.14
First quartile of skewness among attributes of the numeric type.
7.85
Mean of means among attributes of the numeric type.
0.32
First quartile of standard deviation of attributes of the numeric type.
0.11
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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