Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4892

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4892

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: CHEMBL4892 (TID: 10651), and it has 235 rows and 65 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.

67 features

pXC50 (target)numeric178 unique values
0 missing
molecule_id (row identifier)nominal235 unique values
0 missing
P_VSA_MR_6numeric170 unique values
0 missing
nBMnumeric20 unique values
0 missing
Ucnumeric20 unique values
0 missing
P_VSA_s_4numeric149 unique values
0 missing
nCsp2numeric19 unique values
0 missing
Eta_betaPnumeric38 unique values
0 missing
P_VSA_e_2numeric196 unique values
0 missing
Uinumeric22 unique values
0 missing
NRSnumeric5 unique values
0 missing
nCarnumeric19 unique values
0 missing
NaaCHnumeric15 unique values
0 missing
P_VSA_p_3numeric196 unique values
0 missing
P_VSA_v_3numeric196 unique values
0 missing
C.024numeric14 unique values
0 missing
NaasCnumeric10 unique values
0 missing
P_VSA_m_2numeric197 unique values
0 missing
P_VSA_i_2numeric196 unique values
0 missing
Eta_FLnumeric198 unique values
0 missing
piPC03numeric141 unique values
0 missing
Eta_F_Anumeric176 unique values
0 missing
SM04_EA.bo.numeric156 unique values
0 missing
GATS2inumeric173 unique values
0 missing
SaaCHnumeric219 unique values
0 missing
SpMax3_Bh.p.numeric145 unique values
0 missing
SpMax3_Bh.v.numeric138 unique values
0 missing
GGI6numeric132 unique values
0 missing
Eta_beta_Anumeric130 unique values
0 missing
Eta_betaP_Anumeric96 unique values
0 missing
SpMAD_EA.bo.numeric127 unique values
0 missing
Eig08_AEA.ed.numeric126 unique values
0 missing
Eta_Fnumeric218 unique values
0 missing
SM06_AEA.bo.numeric161 unique values
0 missing
S3Knumeric198 unique values
0 missing
SpMax4_Bh.m.numeric168 unique values
0 missing
SM05_AEA.bo.numeric157 unique values
0 missing
SsssNnumeric178 unique values
0 missing
SM04_AEA.bo.numeric152 unique values
0 missing
Eig04_EAnumeric142 unique values
0 missing
SM12_AEA.bo.numeric142 unique values
0 missing
MWC08numeric148 unique values
0 missing
MWC09numeric153 unique values
0 missing
SM03_EA.bo.numeric71 unique values
0 missing
SRW10numeric147 unique values
0 missing
TWCnumeric146 unique values
0 missing
Eig05_AEA.bo.numeric143 unique values
0 missing
Eig05_EA.bo.numeric141 unique values
0 missing
SM15_AEA.ri.numeric141 unique values
0 missing
ATS2vnumeric187 unique values
0 missing
Polnumeric47 unique values
0 missing
X4solnumeric163 unique values
0 missing
Eig05_EA.ed.numeric133 unique values
0 missing
MWC07numeric145 unique values
0 missing
SM03_EA.ri.numeric151 unique values
0 missing
SM04_AEA.ed.numeric149 unique values
0 missing
SM05_AEA.ed.numeric149 unique values
0 missing
SM06_EAnumeric141 unique values
0 missing
SM14_AEA.dm.numeric133 unique values
0 missing
SM03_AEA.bo.numeric143 unique values
0 missing
SM06_AEA.ed.numeric147 unique values
0 missing
SM07_AEA.ed.numeric150 unique values
0 missing
Eig06_AEA.ed.numeric126 unique values
0 missing
Eig04_AEA.bo.numeric144 unique values
0 missing
Eig12_EAnumeric108 unique values
0 missing
Eig12_EA.ri.numeric151 unique values
0 missing
SM06_AEA.dm.numeric108 unique values
0 missing

62 properties

235
Number of instances (rows) of the dataset.
67
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.
66
Number of numeric attributes.
1
Number of nominal attributes.
-0.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.29
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
5.75
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.
-0.61
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.
5.24
Mean standard deviation of attributes of the numeric type.
-0.82
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.
0.53
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.31
Minimum kurtosis among attributes of the numeric type.
0.25
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.06
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.42
Third quartile of kurtosis among attributes of the numeric type.
137.93
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.51
Percentage of numeric attributes.
11.21
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-1.5
Minimum skewness among attributes of the numeric type.
1.49
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.
0.09
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
-0.11
Third quartile of skewness among attributes of the numeric type.
0.57
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.47
First quartile of kurtosis among attributes of the numeric type.
2.5
Third quartile of standard deviation of attributes of the numeric type.
41.2
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
3.37
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.
-0.01
Mean kurtosis among attributes of the numeric type.
-1.08
First quartile of skewness among attributes of the numeric type.
17.64
Mean of means among attributes of the numeric type.
0.4
First quartile of standard deviation of attributes of the numeric type.
-0.09
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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