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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903

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: CHEMBL5903 (TID: 101598), and it has 399 rows and 68 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.

70 features

pXC50 (target)numeric57 unique values
0 missing
SM06_AEA.ri.numeric314 unique values
0 missing
Chi1_EA.ri.numeric387 unique values
0 missing
Eig11_EA.ed.numeric314 unique values
0 missing
SpMaxA_EA.ri.numeric123 unique values
0 missing
GGI7numeric270 unique values
0 missing
IDEnumeric334 unique values
0 missing
X2vnumeric375 unique values
0 missing
SpAD_AEA.ed.numeric366 unique values
0 missing
Chi0_EA.ed.numeric358 unique values
0 missing
SpMax3_Bh.v.numeric283 unique values
0 missing
Vxnumeric360 unique values
0 missing
VvdwMGnumeric360 unique values
0 missing
SpMax3_Bh.i.numeric275 unique values
0 missing
MDDDnumeric363 unique values
0 missing
SpMax3_Bh.e.numeric280 unique values
0 missing
ECCnumeric278 unique values
0 missing
ATS7vnumeric350 unique values
0 missing
Eig05_AEA.ri.numeric327 unique values
0 missing
SpMin8_Bh.p.numeric275 unique values
0 missing
SM04_AEA.ri.numeric316 unique values
0 missing
RDSQnumeric368 unique values
0 missing
RDCHInumeric349 unique values
0 missing
Psi_i_0numeric376 unique values
0 missing
Eig09_EA.ri.numeric302 unique values
0 missing
Eig09_EA.ed.numeric316 unique values
0 missing
Eta_Lnumeric379 unique values
0 missing
ATS8mnumeric354 unique values
0 missing
IDMTnumeric368 unique values
0 missing
XMODnumeric380 unique values
0 missing
Svnumeric369 unique values
0 missing
SM03_AEA.dm.numeric273 unique values
0 missing
Eig09_EAnumeric273 unique values
0 missing
Eig10_AEA.ri.numeric324 unique values
0 missing
CENTnumeric331 unique values
0 missing
MWnumeric367 unique values
0 missing
Eig09_EA.bo.numeric306 unique values
0 missing
SM04_AEA.dm.numeric297 unique values
0 missing
Eig10_EAnumeric297 unique values
0 missing
SM05_AEA.ri.numeric324 unique values
0 missing
Eig10_EA.ed.numeric324 unique values
0 missing
SpMax3_Bh.m.numeric282 unique values
0 missing
GMTIVnumeric388 unique values
0 missing
SpMax3_Bh.p.numeric282 unique values
0 missing
Chi0_EA.bo.numeric362 unique values
0 missing
X1vnumeric373 unique values
0 missing
VARnumeric184 unique values
0 missing
SpMax5_Bh.m.numeric308 unique values
0 missing
Eig10_EA.bo.numeric307 unique values
0 missing
Eig04_EA.ri.numeric328 unique values
0 missing
SM12_AEA.bo.numeric306 unique values
0 missing
Eig04_EAnumeric306 unique values
0 missing
Eig04_AEA.ri.numeric327 unique values
0 missing
Eta_alphanumeric322 unique values
0 missing
S1Knumeric346 unique values
0 missing
IDETnumeric368 unique values
0 missing
Uindexnumeric368 unique values
0 missing
X0solnumeric299 unique values
0 missing
X0vnumeric375 unique values
0 missing
MSDnumeric359 unique values
0 missing
ATSC7mnumeric384 unique values
0 missing
Eig10_EA.ri.numeric317 unique values
0 missing
molecule_id (row identifier)nominal399 unique values
0 missing
ATS7mnumeric347 unique values
0 missing
SpMax6_Bh.m.numeric304 unique values
0 missing
Xunumeric366 unique values
0 missing
SMTIVnumeric388 unique values
0 missing
GMTInumeric365 unique values
0 missing
Eig04_AEA.bo.numeric295 unique values
0 missing
CSInumeric315 unique values
0 missing

62 properties

399
Number of instances (rows) of the dataset.
70
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.
69
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.
98.57
Percentage of numeric attributes.
1.43
Percentage of nominal attributes.
First quartile of entropy among attributes.
-0.39
First quartile of kurtosis among attributes of the numeric type.
2.26
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
-1.11
First quartile of skewness among attributes of the numeric type.
0.6
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.56
Second quartile (Median) of kurtosis among attributes of the numeric type.
3.74
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.
0.13
Second quartile (Median) of skewness among attributes of the numeric type.
2.06
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
1.53
Third quartile of kurtosis among attributes of the numeric type.
29.12
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
Third quartile of skewness among attributes of the numeric type.
13.87
Third quartile of standard deviation of attributes of the numeric type.
0.31
Average class difference between consecutive instances.
1292.71
Mean of means among attributes of the numeric type.
Entropy of the target attribute values.
0.18
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.
51.45
Maximum kurtosis among attributes of the numeric type.
32399.15
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.
3.82
Maximum skewness among attributes of the numeric type.
25116.58
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
1.62
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.
-0.17
Mean skewness among attributes of the numeric type.
1018.73
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.66
Minimum kurtosis among attributes of the numeric type.
0.11
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.
-2.06
Minimum skewness among attributes of the numeric type.
0.04
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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