Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4899

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4899

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: CHEMBL4899 (TID: 100414), and it has 266 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)numeric148 unique values
0 missing
molecule_id (row identifier)nominal266 unique values
0 missing
nNnumeric10 unique values
0 missing
nTriazolesnumeric2 unique values
0 missing
nArXnumeric6 unique values
0 missing
SpMin1_Bh.e.numeric87 unique values
0 missing
CATS2D_02_DLnumeric11 unique values
0 missing
CATS2D_01_AAnumeric3 unique values
0 missing
Eta_betaPnumeric37 unique values
0 missing
nArNHRnumeric3 unique values
0 missing
SpMax3_Bh.m.numeric169 unique values
0 missing
SpMin1_Bh.v.numeric92 unique values
0 missing
Eig07_AEA.bo.numeric126 unique values
0 missing
X.numeric53 unique values
0 missing
P_VSA_LogP_8numeric14 unique values
0 missing
P_VSA_p_3numeric235 unique values
0 missing
P_VSA_v_3numeric235 unique values
0 missing
nXnumeric8 unique values
0 missing
C.026numeric13 unique values
0 missing
P_VSA_LogP_6numeric43 unique values
0 missing
nABnumeric20 unique values
0 missing
nBMnumeric22 unique values
0 missing
Ucnumeric22 unique values
0 missing
Uinumeric21 unique values
0 missing
PCDnumeric172 unique values
0 missing
piPC09numeric168 unique values
0 missing
SssNHnumeric172 unique values
0 missing
MATS7mnumeric196 unique values
0 missing
N.070numeric3 unique values
0 missing
P_VSA_m_2numeric235 unique values
0 missing
P_VSA_LogP_5numeric175 unique values
0 missing
SsClnumeric112 unique values
0 missing
Eig07_EA.bo.numeric134 unique values
0 missing
piIDnumeric163 unique values
0 missing
MATS4inumeric202 unique values
0 missing
SpMax4_Bh.m.numeric185 unique values
0 missing
JGI4numeric33 unique values
0 missing
P_VSA_e_3numeric98 unique values
0 missing
CATS2D_04_DAnumeric6 unique values
0 missing
NaasNnumeric3 unique values
0 missing
SaasNnumeric48 unique values
0 missing
GGI7numeric143 unique values
0 missing
ATS8mnumeric236 unique values
0 missing
P_VSA_MR_6numeric180 unique values
0 missing
NaasCnumeric13 unique values
0 missing
IC1numeric213 unique values
0 missing
Eta_F_Anumeric199 unique values
0 missing
P_VSA_i_4numeric96 unique values
0 missing
Eig06_AEA.bo.numeric128 unique values
0 missing
ATS7mnumeric233 unique values
0 missing
ZM1MulPernumeric240 unique values
0 missing
ZM1Vnumeric124 unique values
0 missing
CATS2D_05_DDnumeric3 unique values
0 missing
Eta_FLnumeric246 unique values
0 missing
Eig04_AEA.ri.numeric200 unique values
0 missing
SpMax4_Bh.v.numeric178 unique values
0 missing
StNnumeric114 unique values
0 missing
IC2numeric209 unique values
0 missing
MATS6enumeric192 unique values
0 missing
P_VSA_MR_7numeric46 unique values
0 missing
piPC05numeric147 unique values
0 missing
SM04_EA.bo.numeric161 unique values
0 missing
SpMin1_Bh.p.numeric94 unique values
0 missing
SsFnumeric108 unique values
0 missing
F.084numeric4 unique values
0 missing
D.Dtr10numeric96 unique values
0 missing
piPC03numeric147 unique values
0 missing
GATS7vnumeric183 unique values
0 missing

62 properties

266
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.
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.
Second quartile (Median) of entropy among attributes.
0.26
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
2.06
Second quartile (Median) of kurtosis 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.
0.13
Mean skewness among attributes of the numeric type.
4.01
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
11.96
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.
-0.09
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.92
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.75
Second quartile (Median) of standard deviation of attributes of the numeric type.
51.57
Maximum kurtosis among attributes of the numeric type.
-0.02
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
524.57
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.
5.15
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.
98.53
Percentage of numeric attributes.
11.98
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-2.79
Minimum skewness among attributes of the numeric type.
1.47
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6.9
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.89
Third quartile of skewness among attributes of the numeric type.
132.71
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.37
First quartile of kurtosis among attributes of the numeric type.
4.81
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.
1.57
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
4.36
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.
35.92
Mean of means among attributes of the numeric type.
-1.03
First quartile of skewness among attributes of the numeric type.
-0.08
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.29
First quartile of standard deviation of attributes of the numeric type.

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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