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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5645

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5645

deactivated ARFF Publicly available Visibility: public Uploaded 15-07-2016 by unknown
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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: CHEMBL5645 (TID: 102414), and it has 523 rows and 65 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.

67 features

pXC50 (target)numeric312 unique values
0 missing
GGI8numeric269 unique values
0 missing
GGI10numeric214 unique values
0 missing
SpMax2_Bh.p.numeric163 unique values
0 missing
Eta_betanumeric117 unique values
0 missing
SM06_AEA.ri.numeric267 unique values
0 missing
Eig11_EA.ed.numeric267 unique values
0 missing
P_VSA_i_2numeric384 unique values
0 missing
GGI7numeric254 unique values
0 missing
SpMaxA_AEA.bo.numeric72 unique values
0 missing
GGI1numeric24 unique values
0 missing
SM07_AEA.ri.numeric246 unique values
0 missing
Eig12_EA.ed.numeric246 unique values
0 missing
Eig11_AEA.bo.numeric236 unique values
0 missing
Ucnumeric29 unique values
0 missing
nBMnumeric29 unique values
0 missing
X3numeric353 unique values
0 missing
Eig10_EA.bo.numeric255 unique values
0 missing
GMTInumeric370 unique values
0 missing
SMTInumeric372 unique values
0 missing
Eig11_EAnumeric210 unique values
0 missing
SM05_AEA.dm.numeric210 unique values
0 missing
Chi0_EA.ed.numeric352 unique values
0 missing
P_VSA_s_4numeric177 unique values
0 missing
Eig11_AEA.ri.numeric248 unique values
0 missing
Eig11_EA.ri.numeric247 unique values
0 missing
Eta_betaSnumeric73 unique values
0 missing
Eig12_EAnumeric212 unique values
0 missing
SM06_AEA.dm.numeric212 unique values
0 missing
IDETnumeric373 unique values
0 missing
TIC1numeric398 unique values
0 missing
UNIPnumeric163 unique values
0 missing
IDMTnumeric373 unique values
0 missing
Xunumeric364 unique values
0 missing
SM02_EA.bo.numeric171 unique values
0 missing
Eig15_AEA.ri.numeric281 unique values
0 missing
SpMaxA_EAnumeric51 unique values
0 missing
P_VSA_e_2numeric385 unique values
0 missing
Eig09_AEA.ri.numeric317 unique values
0 missing
SM02_AEA.ri.numeric281 unique values
0 missing
Eig07_EA.ed.numeric281 unique values
0 missing
Eig09_AEA.ed.numeric232 unique values
0 missing
Eig06_AEA.ri.numeric299 unique values
0 missing
SpMaxA_EA.ri.numeric57 unique values
0 missing
piPC02numeric171 unique values
0 missing
P_VSA_v_3numeric385 unique values
0 missing
P_VSA_p_3numeric385 unique values
0 missing
SM04_AEA.ri.numeric296 unique values
0 missing
Eig09_EA.ed.numeric296 unique values
0 missing
SM03_AEA.dm.numeric249 unique values
0 missing
Eig09_EAnumeric249 unique values
0 missing
molecule_id (row identifier)nominal523 unique values
0 missing
Eig10_AEA.bo.numeric242 unique values
0 missing
SpMax2_Bh.m.numeric198 unique values
0 missing
SpMaxA_AEA.ri.numeric69 unique values
0 missing
RDCHInumeric309 unique values
0 missing
SM04_EA.bo.numeric306 unique values
0 missing
Eig10_AEA.ed.numeric247 unique values
0 missing
Eig15_EAnumeric214 unique values
0 missing
Eig15_EA.ri.numeric281 unique values
0 missing
SM09_AEA.dm.numeric214 unique values
0 missing
piPC03numeric255 unique values
0 missing
Eig07_AEA.ed.numeric235 unique values
0 missing
Uinumeric30 unique values
0 missing
Eig11_EA.bo.numeric241 unique values
0 missing
SpMaxA_AEA.ed.numeric93 unique values
0 missing
SM02_EA.ri.numeric291 unique values
0 missing

62 properties

523
Number of instances (rows) of the dataset.
67
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.
66
Number of numeric attributes.
1
Number of nominal attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
98.51
Percentage of numeric attributes.
1.49
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.37
First quartile of kurtosis among attributes of the numeric type.
1.53
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
-1.23
First quartile of skewness among attributes of the numeric type.
0.29
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
1.24
Second quartile (Median) of kurtosis among attributes of the numeric type.
4.05
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-0.72
Second quartile (Median) of skewness among attributes of the numeric type.
0.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
4.83
Third quartile of kurtosis among attributes of the numeric type.
13.27
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
-0.16
Third quartile of skewness among attributes of the numeric type.
2.27
Third quartile of standard deviation of attributes of the numeric type.
-0.22
Average class difference between consecutive instances.
1454.6
Mean of means among attributes of the numeric type.
Entropy of the target attribute values.
0.13
Number of attributes divided by the number of instances.
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.
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
9.88
Maximum kurtosis among attributes of the numeric type.
46611.16
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
2.28
Maximum skewness among attributes of the numeric type.
18838.21
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
2.64
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Average number of distinct values among the attributes of the nominal type.
-0.6
Mean skewness among attributes of the numeric type.
547.05
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.89
Minimum kurtosis among attributes of the numeric type.
0.08
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
-2.41
Minimum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.

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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