Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2336

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2336

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: CHEMBL2336 (TID: 12536), and it has 202 rows and 64 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.

66 features

pXC50 (target)numeric145 unique values
0 missing
molecule_id (row identifier)nominal202 unique values
0 missing
Eig11_AEA.ed.numeric92 unique values
0 missing
Eig11_EA.ed.numeric101 unique values
0 missing
SM06_AEA.ri.numeric101 unique values
0 missing
MPC06numeric69 unique values
0 missing
Eig10_AEA.ri.numeric118 unique values
0 missing
Eig10_EA.ri.numeric110 unique values
0 missing
MPC10numeric98 unique values
0 missing
SpMaxA_AEA.dm.numeric62 unique values
0 missing
ATSC6inumeric188 unique values
0 missing
Eig11_EAnumeric90 unique values
0 missing
SM05_AEA.dm.numeric90 unique values
0 missing
TPCnumeric112 unique values
0 missing
Eig10_EA.bo.numeric88 unique values
0 missing
Eig10_EAnumeric91 unique values
0 missing
SM04_AEA.dm.numeric91 unique values
0 missing
SM03_AEA.ed.numeric105 unique values
0 missing
Wapnumeric118 unique values
0 missing
Eig13_AEA.bo.numeric96 unique values
0 missing
SpDiam_EA.ed.numeric94 unique values
0 missing
SM02_EA.ri.numeric149 unique values
0 missing
SRW08numeric109 unique values
0 missing
MWC04numeric101 unique values
0 missing
SRW06numeric98 unique values
0 missing
SpAD_EA.ri.numeric178 unique values
0 missing
ATSC5inumeric184 unique values
0 missing
MWC07numeric110 unique values
0 missing
MPC04numeric50 unique values
0 missing
X5numeric124 unique values
0 missing
SpAD_AEA.dm.numeric164 unique values
0 missing
RDSQnumeric124 unique values
0 missing
SpAD_AEA.ri.numeric178 unique values
0 missing
SpAD_EAnumeric124 unique values
0 missing
SpAD_AEA.bo.numeric138 unique values
0 missing
ATS5mnumeric179 unique values
0 missing
SpAD_EA.ed.numeric124 unique values
0 missing
MWC05numeric103 unique values
0 missing
Eig15_AEA.ri.numeric129 unique values
0 missing
Eig15_EA.ri.numeric137 unique values
0 missing
Chi0_EA.ed.numeric124 unique values
0 missing
Chi1_EA.ed.numeric122 unique values
0 missing
MPC07numeric81 unique values
0 missing
SM02_AEA.ed.numeric81 unique values
0 missing
Eig11_AEA.bo.numeric97 unique values
0 missing
SpMax5_Bh.e.numeric123 unique values
0 missing
MWC03numeric81 unique values
0 missing
ZM2numeric81 unique values
0 missing
Eig01_AEA.ed.numeric67 unique values
0 missing
Eig01_EA.ed.numeric80 unique values
0 missing
SM09_EA.ed.numeric118 unique values
0 missing
SM10_AEA.dm.numeric80 unique values
0 missing
SM10_EA.ed.numeric115 unique values
0 missing
SM11_EA.ed.numeric114 unique values
0 missing
SM12_EA.ed.numeric113 unique values
0 missing
SM13_EA.ed.numeric113 unique values
0 missing
SM14_EA.ed.numeric110 unique values
0 missing
SM15_EA.ed.numeric105 unique values
0 missing
SpMax_AEA.ed.numeric67 unique values
0 missing
SpMax_EA.ed.numeric80 unique values
0 missing
MWC02numeric49 unique values
0 missing
ZM1numeric49 unique values
0 missing
SM04_EA.ed.numeric116 unique values
0 missing
ATS6mnumeric178 unique values
0 missing
MPC03numeric42 unique values
0 missing
Eig01_EAnumeric75 unique values
0 missing

62 properties

202
Number of instances (rows) of the dataset.
66
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.
65
Number of numeric attributes.
1
Number of nominal attributes.
0.9
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.33
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
7.08
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.
-0.81
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.
313.1
Mean standard deviation of attributes of the numeric type.
-0.67
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.
0.41
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.51
Minimum kurtosis among attributes of the numeric type.
0.11
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
12.28
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
3.4
Third quartile of kurtosis among attributes of the numeric type.
39118.23
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.48
Percentage of numeric attributes.
23.96
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-2.94
Minimum skewness among attributes of the numeric type.
1.52
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.
0.02
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.21
Third quartile of skewness among attributes of the numeric type.
0.99
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.2
First quartile of kurtosis among attributes of the numeric type.
0.99
Third quartile of standard deviation of attributes of the numeric type.
20144.5
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
3.02
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.
2.55
Mean kurtosis among attributes of the numeric type.
-1.44
First quartile of skewness among attributes of the numeric type.
625.96
Mean of means among attributes of the numeric type.
0.23
First quartile of standard deviation of attributes of the numeric type.
-0.21
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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