Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4018

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4018

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: CHEMBL4018 (TID: 10477), and it has 682 rows and 70 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.

72 features

pXC50 (target)numeric378 unique values
0 missing
molecule_id (row identifier)nominal682 unique values
0 missing
CATS2D_07_DAnumeric18 unique values
0 missing
CATS2D_07_APnumeric7 unique values
0 missing
CATS2D_05_DDnumeric11 unique values
0 missing
CIC5numeric475 unique values
0 missing
GATS2pnumeric342 unique values
0 missing
H.052numeric52 unique values
0 missing
GATS2inumeric366 unique values
0 missing
Eig08_EA.dm.numeric63 unique values
0 missing
Eig09_EA.dm.numeric57 unique values
0 missing
Eig11_EA.dm.numeric48 unique values
0 missing
Eig12_EA.dm.numeric52 unique values
0 missing
Eig13_EA.dm.numeric42 unique values
0 missing
Eig14_EA.dm.numeric43 unique values
0 missing
Eig15_EA.dm.numeric46 unique values
0 missing
NdOnumeric29 unique values
0 missing
CATS2D_08_AAnumeric36 unique values
0 missing
CATS2D_09_DAnumeric38 unique values
0 missing
CATS2D_09_PLnumeric15 unique values
0 missing
CATS2D_04_ALnumeric51 unique values
0 missing
CATS2D_04_DPnumeric3 unique values
0 missing
C.042numeric3 unique values
0 missing
nRCONH2numeric6 unique values
0 missing
C.002numeric33 unique values
0 missing
C.040numeric27 unique values
0 missing
CATS2D_00_DDnumeric13 unique values
0 missing
CATS2D_00_DPnumeric13 unique values
0 missing
CATS2D_00_PPnumeric13 unique values
0 missing
CATS2D_02_APnumeric11 unique values
0 missing
CATS2D_02_DPnumeric7 unique values
0 missing
CATS2D_03_DDnumeric25 unique values
0 missing
CATS2D_06_DAnumeric35 unique values
0 missing
CATS2D_07_DDnumeric18 unique values
0 missing
CATS2D_07_DPnumeric10 unique values
0 missing
CATS2D_08_APnumeric14 unique values
0 missing
CATS2D_08_DDnumeric20 unique values
0 missing
CATS2D_08_DLnumeric51 unique values
0 missing
CATS2D_08_DPnumeric12 unique values
0 missing
CATS2D_09_ALnumeric58 unique values
0 missing
CATS2D_09_APnumeric14 unique values
0 missing
Eig06_EA.dm.numeric92 unique values
0 missing
Eig10_EA.dm.numeric56 unique values
0 missing
N.066numeric10 unique values
0 missing
nC..N.N2numeric6 unique values
0 missing
nRNH2numeric10 unique values
0 missing
NsNH2numeric13 unique values
0 missing
P_VSA_e_3numeric248 unique values
0 missing
P_VSA_i_4numeric271 unique values
0 missing
P_VSA_LogP_4numeric300 unique values
0 missing
SdssCnumeric502 unique values
0 missing
SsNH2numeric146 unique values
0 missing
CATS2D_07_DLnumeric46 unique values
0 missing
CATS2D_09_DDnumeric33 unique values
0 missing
CATS2D_09_DPnumeric8 unique values
0 missing
CATS2D_08_PLnumeric8 unique values
0 missing
CATS2D_09_AAnumeric19 unique values
0 missing
CATS2D_05_PLnumeric17 unique values
0 missing
Eta_F_Anumeric450 unique values
0 missing
DLS_consnumeric44 unique values
0 missing
C.008numeric27 unique values
0 missing
CATS2D_08_ALnumeric53 unique values
0 missing
nCsnumeric43 unique values
0 missing
nOnumeric34 unique values
0 missing
NsssCHnumeric30 unique values
0 missing
SsssCHnumeric389 unique values
0 missing
CATS2D_02_PPnumeric4 unique values
0 missing
C.041numeric6 unique values
0 missing
GATS2vnumeric304 unique values
0 missing
ATSC1enumeric275 unique values
0 missing
Eig02_AEA.ri.numeric391 unique values
0 missing
CATS2D_06_DPnumeric7 unique values
0 missing

62 properties

682
Number of instances (rows) of the dataset.
72
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.
71
Number of numeric attributes.
1
Number of nominal attributes.
3.4
Maximum skewness among attributes of the numeric type.
0.13
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
2.29
Third quartile of skewness among attributes of the numeric type.
330.88
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
1.37
First quartile of kurtosis among attributes of the numeric type.
8.66
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.57
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
3.3
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
9.97
Mean of means among attributes of the numeric type.
1.62
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.76
First quartile of standard deviation of attributes of the numeric type.
0.21
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
Average number of distinct values among the attributes of the nominal type.
2.63
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.11
Number of attributes divided by the number of instances.
1.72
Mean skewness among attributes of the numeric type.
1.65
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.
Percentage of instances belonging to the most frequent class.
17.4
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.89
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.37
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
3.5
Second quartile (Median) of standard deviation of attributes of the numeric type.
13.22
Maximum kurtosis among attributes of the numeric type.
-6.1
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
197.8
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
5.22
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
98.61
Percentage of numeric attributes.
4.74
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-2.83
Minimum skewness among attributes of the numeric type.
1.39
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.

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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