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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4649

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4649

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: CHEMBL4649 (TID: 10796), and it has 536 rows and 66 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.

68 features

pXC50 (target)numeric301 unique values
0 missing
C.numeric97 unique values
0 missing
SM13_AEA.ri.numeric237 unique values
0 missing
D.Dtr06numeric316 unique values
0 missing
SpMaxA_AEA.dm.numeric78 unique values
0 missing
DLS_04numeric7 unique values
0 missing
P_VSA_s_3numeric359 unique values
0 missing
SdOnumeric279 unique values
0 missing
SpAD_EA.bo.numeric345 unique values
0 missing
SpMin2_Bh.e.numeric181 unique values
0 missing
MATS7inumeric329 unique values
0 missing
SpMin1_Bh.v.numeric134 unique values
0 missing
SpMin2_Bh.i.numeric183 unique values
0 missing
nCXr.numeric2 unique values
0 missing
nCconjXnumeric2 unique values
0 missing
C.020numeric2 unique values
0 missing
SpMax2_Bh.p.numeric161 unique values
0 missing
Eig03_EA.bo.numeric237 unique values
0 missing
GATS3inumeric315 unique values
0 missing
SdsCHnumeric112 unique values
0 missing
GATS7inumeric347 unique values
0 missing
SpMin1_Bh.i.numeric141 unique values
0 missing
nCbHnumeric13 unique values
0 missing
SpMax2_Bh.v.numeric155 unique values
0 missing
ARRnumeric65 unique values
0 missing
SpDiam_EA.ed.numeric245 unique values
0 missing
Eig01_EA.bo.numeric164 unique values
0 missing
SM11_AEA.ri.numeric164 unique values
0 missing
SpDiam_EA.bo.numeric164 unique values
0 missing
SpMax_EA.bo.numeric164 unique values
0 missing
P_VSA_s_4numeric234 unique values
0 missing
Eig01_EA.ed.numeric213 unique values
0 missing
SM10_AEA.dm.numeric213 unique values
0 missing
SM15_EA.ed.numeric269 unique values
0 missing
P_VSA_LogP_3numeric37 unique values
0 missing
CATS2D_02_LLnumeric27 unique values
0 missing
SpMin3_Bh.i.numeric216 unique values
0 missing
nBnznumeric4 unique values
0 missing
ATSC3enumeric358 unique values
0 missing
SpMax1_Bh.p.numeric187 unique values
0 missing
Uinumeric20 unique values
0 missing
nCsp2numeric20 unique values
0 missing
C.024numeric14 unique values
0 missing
PCRnumeric153 unique values
0 missing
RBFnumeric100 unique values
0 missing
NaaCHnumeric14 unique values
0 missing
SaaCHnumeric469 unique values
0 missing
Eta_beta_Anumeric169 unique values
0 missing
SpMAD_EA.bo.numeric202 unique values
0 missing
nCarnumeric16 unique values
0 missing
Eta_betaP_Anumeric107 unique values
0 missing
nCb.numeric11 unique values
0 missing
X3vnumeric445 unique values
0 missing
MATS1pnumeric193 unique values
0 missing
CATS2D_03_AAnumeric7 unique values
0 missing
MATS1vnumeric128 unique values
0 missing
NssCH2numeric12 unique values
0 missing
PDInumeric107 unique values
0 missing
nCsp3numeric17 unique values
0 missing
Minumeric55 unique values
0 missing
molecule_id (row identifier)nominal536 unique values
0 missing
Eta_C_Anumeric257 unique values
0 missing
SssCH2numeric433 unique values
0 missing
SpMaxA_EA.ed.numeric120 unique values
0 missing
SpMax1_Bh.v.numeric185 unique values
0 missing
SpMin3_Bh.e.numeric224 unique values
0 missing
RBNnumeric14 unique values
0 missing
MATS3inumeric282 unique values
0 missing

62 properties

536
Number of instances (rows) of the dataset.
68
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.
67
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.
98.53
Percentage of numeric attributes.
1.47
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.07
First quartile of kurtosis among attributes of the numeric type.
0.95
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
0.21
First quartile of skewness among attributes of the numeric type.
0.09
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
1.01
Second quartile (Median) of kurtosis among attributes of the numeric type.
2.98
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.
0.65
Second quartile (Median) of skewness among attributes of the numeric type.
0.31
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
12.92
Third quartile of kurtosis among attributes of the numeric type.
6.94
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
2.07
Third quartile of skewness among attributes of the numeric type.
2.19
Third quartile of standard deviation of attributes of the numeric type.
0.09
Average class difference between consecutive instances.
10.25
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.
129.24
Maximum kurtosis among attributes of the numeric type.
200.41
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.
11.29
Maximum skewness among attributes of the numeric type.
139.86
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
21.71
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.
2.12
Mean skewness among attributes of the numeric type.
6.05
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.13
Minimum kurtosis among attributes of the numeric type.
-0.1
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.
-6.3
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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