Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL276

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL276

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: CHEMBL276 (TID: 10647), and it has 890 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)numeric602 unique values
0 missing
molecule_id (row identifier)nominal890 unique values
0 missing
H.052numeric15 unique values
0 missing
GATS6enumeric631 unique values
0 missing
GATS5snumeric640 unique values
0 missing
GATS6snumeric650 unique values
0 missing
JGI1numeric149 unique values
0 missing
RBFnumeric141 unique values
0 missing
SaaNnumeric319 unique values
0 missing
MATS4vnumeric344 unique values
0 missing
O.numeric85 unique values
0 missing
MATS6enumeric432 unique values
0 missing
JGI6numeric35 unique values
0 missing
Rbridnumeric8 unique values
0 missing
MATS6snumeric442 unique values
0 missing
SaasCnumeric608 unique values
0 missing
GATS5enumeric602 unique values
0 missing
N.075numeric5 unique values
0 missing
NaaNnumeric5 unique values
0 missing
GATS8vnumeric456 unique values
0 missing
GATS4vnumeric370 unique values
0 missing
JGI3numeric73 unique values
0 missing
MATS8vnumeric445 unique values
0 missing
MATS7enumeric428 unique values
0 missing
nCsnumeric18 unique values
0 missing
MATS3vnumeric312 unique values
0 missing
MATS3enumeric338 unique values
0 missing
C.002numeric17 unique values
0 missing
GATS1snumeric343 unique values
0 missing
H.046numeric19 unique values
0 missing
Eta_sh_xnumeric69 unique values
0 missing
Eta_epsi_Anumeric172 unique values
0 missing
Menumeric76 unique values
0 missing
JGTnumeric286 unique values
0 missing
MATS3snumeric329 unique values
0 missing
MATS7snumeric445 unique values
0 missing
MATS3mnumeric334 unique values
0 missing
SpDiam_EA.bo.numeric330 unique values
0 missing
MATS8pnumeric433 unique values
0 missing
SpMin2_Bh.s.numeric343 unique values
0 missing
SpMaxA_EA.dm.numeric159 unique values
0 missing
JGI5numeric44 unique values
0 missing
NaaSnumeric2 unique values
0 missing
GATS6mnumeric508 unique values
0 missing
GATS8enumeric584 unique values
0 missing
MATS1snumeric309 unique values
0 missing
X0Anumeric88 unique values
0 missing
H.numeric172 unique values
0 missing
Eig07_AEA.bo.numeric437 unique values
0 missing
SM06_EA.bo.numeric556 unique values
0 missing
Eta_sh_pnumeric223 unique values
0 missing
H.051numeric7 unique values
0 missing
Eig15_EAnumeric366 unique values
0 missing
SM09_AEA.dm.numeric366 unique values
0 missing
SM05_AEA.bo.numeric507 unique values
0 missing
SM08_EA.bo.numeric541 unique values
0 missing
ARRnumeric111 unique values
0 missing
SRW09numeric85 unique values
0 missing
P_VSA_m_3numeric82 unique values
0 missing
Depressant.80numeric2 unique values
0 missing
SM15_EA.dm.numeric133 unique values
0 missing
Eig15_EA.ri.numeric481 unique values
0 missing
piPC04numeric427 unique values
0 missing
ATSC7mnumeric779 unique values
0 missing
JGI2numeric71 unique values
0 missing
SpDiam_EA.dm.numeric117 unique values
0 missing
nBMnumeric34 unique values
0 missing
Ucnumeric34 unique values
0 missing
GATS4pnumeric397 unique values
0 missing
GATS3vnumeric323 unique values
0 missing

62 properties

890
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.
1.06
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.08
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.89
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.
0.52
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.
1.71
Mean standard deviation of attributes of the numeric type.
0.6
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.
0.36
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.84
Minimum kurtosis among attributes of the numeric type.
-0.57
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
13.94
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
3.97
Third quartile of kurtosis among attributes of the numeric type.
49.89
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.57
Percentage of numeric attributes.
3.15
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-1.43
Minimum skewness among attributes of the numeric type.
1.43
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.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.33
Third quartile of skewness among attributes of the numeric type.
2.89
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.24
First quartile of kurtosis among attributes of the numeric type.
1.17
Third quartile of standard deviation of attributes of the numeric type.
34.11
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
0.02
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.
2.34
Mean kurtosis among attributes of the numeric type.
-0.38
First quartile of skewness among attributes of the numeric type.
3.44
Mean of means among attributes of the numeric type.
0.11
First quartile of standard deviation of attributes of the numeric type.
-0.19
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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