Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2285

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2285

deactivated ARFF Publicly available Visibility: public Uploaded 16-07-2016 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: CHEMBL2285 (TID: 12862), and it has 395 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)numeric231 unique values
0 missing
molecule_id (row identifier)nominal395 unique values
0 missing
Eig02_AEA.ed.numeric146 unique values
0 missing
SM05_EA.ri.numeric307 unique values
0 missing
SM06_EA.ri.numeric327 unique values
0 missing
SM07_EA.ri.numeric330 unique values
0 missing
SM08_EA.ri.numeric330 unique values
0 missing
SM09_EA.ri.numeric340 unique values
0 missing
SM10_EA.ri.numeric348 unique values
0 missing
C.011numeric3 unique values
0 missing
SM11_EA.ri.numeric344 unique values
0 missing
SRW07numeric33 unique values
0 missing
SRW09numeric73 unique values
0 missing
SRW05numeric11 unique values
0 missing
SM04_AEA.ed.numeric238 unique values
0 missing
D.Dtr03numeric48 unique values
0 missing
nR03numeric3 unique values
0 missing
SRW03numeric3 unique values
0 missing
JGI2numeric83 unique values
0 missing
Eig02_EA.ri.numeric203 unique values
0 missing
nCrtnumeric5 unique values
0 missing
Eta_sh_xnumeric74 unique values
0 missing
nCtnumeric5 unique values
0 missing
CATS2D_02_NLnumeric3 unique values
0 missing
SddssSnumeric153 unique values
0 missing
SM04_EAnumeric171 unique values
0 missing
ATSC5snumeric389 unique values
0 missing
JGTnumeric180 unique values
0 missing
MWC08numeric250 unique values
0 missing
MWC10numeric253 unique values
0 missing
MWC09numeric257 unique values
0 missing
C.003numeric4 unique values
0 missing
CATS2D_04_ANnumeric4 unique values
0 missing
NddssSnumeric3 unique values
0 missing
S.110numeric3 unique values
0 missing
nSO2Nnumeric3 unique values
0 missing
CATS2D_02_DNnumeric2 unique values
0 missing
MWC07numeric243 unique values
0 missing
SpMax2_Bh.i.numeric155 unique values
0 missing
PW2numeric54 unique values
0 missing
SpDiam_AEA.ri.numeric225 unique values
0 missing
SpMax2_Bh.p.numeric158 unique values
0 missing
SM03_EA.ed.numeric179 unique values
0 missing
SdssCnumeric261 unique values
0 missing
GGI1numeric17 unique values
0 missing
ATS5snumeric323 unique values
0 missing
ZM2Madnumeric367 unique values
0 missing
SM07_EAnumeric241 unique values
0 missing
ATSC4snumeric389 unique values
0 missing
Eig01_AEA.ri.numeric157 unique values
0 missing
SpMax_AEA.ri.numeric157 unique values
0 missing
MWC06numeric249 unique values
0 missing
SM15_EA.ed.numeric206 unique values
0 missing
SpMax2_Bh.e.numeric160 unique values
0 missing
SM05_EA.ed.numeric261 unique values
0 missing
SM10_AEA.ed.numeric262 unique values
0 missing
Eig01_AEA.ed.numeric114 unique values
0 missing
Eig01_EAnumeric137 unique values
0 missing
SM09_AEA.bo.numeric137 unique values
0 missing
SpDiam_EAnumeric137 unique values
0 missing
SpMax_AEA.ed.numeric114 unique values
0 missing
SpMax_EAnumeric137 unique values
0 missing
Eig01_EA.ed.numeric140 unique values
0 missing
SM10_AEA.dm.numeric140 unique values
0 missing
SM13_EA.ed.numeric219 unique values
0 missing
SM14_EA.ed.numeric211 unique values
0 missing

62 properties

395
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.
-0.59
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.17
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
5.93
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.
0.29
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.
4.11
Mean standard deviation of attributes of the numeric type.
0.31
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.
0.56
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.81
Minimum kurtosis among attributes of the numeric type.
-1.85
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.59
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
-0.14
Third quartile of kurtosis among attributes of the numeric type.
258.51
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.
10.77
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-0.97
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.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.51
Third quartile of skewness among attributes of the numeric type.
1.89
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.79
First quartile of kurtosis among attributes of the numeric type.
1.07
Third quartile of standard deviation of attributes of the numeric type.
84.06
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
0.65
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.
-0.42
Mean kurtosis among attributes of the numeric type.
-0.05
First quartile of skewness among attributes of the numeric type.
14.03
Mean of means among attributes of the numeric type.
0.31
First quartile of standard deviation of attributes of the numeric type.
0.33
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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