Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1878

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1878

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: CHEMBL1878 (TID: 282), and it has 479 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)numeric228 unique values
0 missing
molecule_id (row identifier)nominal479 unique values
0 missing
CATS2D_09_LLnumeric33 unique values
0 missing
GGI7numeric252 unique values
0 missing
Eig05_EAnumeric246 unique values
0 missing
SM13_AEA.bo.numeric246 unique values
0 missing
CATS2D_06_ALnumeric22 unique values
0 missing
Eig06_AEA.dm.numeric290 unique values
0 missing
Eig05_AEA.bo.numeric241 unique values
0 missing
GGI4numeric246 unique values
0 missing
ATS4pnumeric348 unique values
0 missing
CATS2D_07_ALnumeric20 unique values
0 missing
Eig05_EA.ri.numeric267 unique values
0 missing
C.011numeric3 unique values
0 missing
ATS4vnumeric340 unique values
0 missing
Eig08_EA.bo.numeric220 unique values
0 missing
Eig04_AEA.dm.numeric257 unique values
0 missing
CATS2D_03_DLnumeric8 unique values
0 missing
H.046numeric20 unique values
0 missing
Eig04_EA.ed.numeric280 unique values
0 missing
SM13_AEA.dm.numeric280 unique values
0 missing
GGI2numeric35 unique values
0 missing
SpMax1_Bh.i.numeric151 unique values
0 missing
ATS5pnumeric373 unique values
0 missing
Eig02_AEA.ri.numeric234 unique values
0 missing
SpMax3_Bh.m.numeric189 unique values
0 missing
SsCH3numeric377 unique values
0 missing
Eig05_AEA.ri.numeric296 unique values
0 missing
Eig03_EA.bo.numeric183 unique values
0 missing
SM13_AEA.ri.numeric183 unique values
0 missing
SRW09numeric27 unique values
0 missing
SpMax1_Bh.e.numeric159 unique values
0 missing
MPC04numeric89 unique values
0 missing
SssNHnumeric292 unique values
0 missing
C.001numeric8 unique values
0 missing
Eig04_EA.ri.numeric283 unique values
0 missing
TPCnumeric243 unique values
0 missing
GGI9numeric207 unique values
0 missing
SpMin2_Bh.e.numeric109 unique values
0 missing
Eig02_AEA.bo.numeric205 unique values
0 missing
SpMax5_Bh.p.numeric246 unique values
0 missing
Eig02_AEA.ed.numeric198 unique values
0 missing
Eig13_EA.bo.numeric239 unique values
0 missing
SM03_EAnumeric19 unique values
0 missing
Eig05_EA.bo.numeric269 unique values
0 missing
SM15_AEA.ri.numeric269 unique values
0 missing
BACnumeric64 unique values
0 missing
GGI5numeric213 unique values
0 missing
Ramnumeric14 unique values
0 missing
Eig08_AEA.bo.numeric214 unique values
0 missing
Eig02_EA.ed.numeric236 unique values
0 missing
SM11_AEA.dm.numeric236 unique values
0 missing
MWC10numeric299 unique values
0 missing
ATS4mnumeric350 unique values
0 missing
SM04_AEA.ed.numeric276 unique values
0 missing
nCpnumeric8 unique values
0 missing
Eta_Bnumeric200 unique values
0 missing
Polnumeric50 unique values
0 missing
TWCnumeric297 unique values
0 missing
ATSC8pnumeric442 unique values
0 missing
X5vnumeric420 unique values
0 missing
SM07_AEA.bo.numeric289 unique values
0 missing
Eta_C_Anumeric313 unique values
0 missing
MWC09numeric311 unique values
0 missing
SM12_AEA.ed.numeric311 unique values
0 missing
Eig13_AEA.bo.numeric227 unique values
0 missing

62 properties

479
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.
0.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.14
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
3.73
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.
0.04
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.
1.44
Mean standard deviation of attributes of the numeric type.
-0.01
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.
0.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.9
Minimum kurtosis among attributes of the numeric type.
0.26
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
8.61
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.51
Third quartile of kurtosis among attributes of the numeric type.
48.8
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.
7.29
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-0.98
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.
0.03
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.48
Third quartile of skewness among attributes of the numeric type.
1.71
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.29
First quartile of kurtosis among attributes of the numeric type.
1.29
Third quartile of standard deviation of attributes of the numeric type.
20.55
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
2.24
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.
0.41
Mean kurtosis among attributes of the numeric type.
-0.46
First quartile of skewness among attributes of the numeric type.
5.98
Mean of means among attributes of the numeric type.
0.24
First quartile of standard deviation of attributes of the numeric type.
0.18
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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