Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4803

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4803

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: CHEMBL4803 (TID: 11573), and it has 713 rows and 68 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.

70 features

pXC50 (target)numeric345 unique values
0 missing
molecule_id (row identifier)nominal713 unique values
0 missing
SaaNnumeric254 unique values
0 missing
N.075numeric6 unique values
0 missing
NaaNnumeric6 unique values
0 missing
C.042numeric3 unique values
0 missing
nImidazolesnumeric3 unique values
0 missing
MATS1inumeric324 unique values
0 missing
CATS2D_04_AAnumeric6 unique values
0 missing
Eta_betaS_Anumeric105 unique values
0 missing
H.049numeric5 unique values
0 missing
nArORnumeric4 unique values
0 missing
O.060numeric4 unique values
0 missing
NssOnumeric4 unique values
0 missing
SssOnumeric143 unique values
0 missing
MATS1pnumeric262 unique values
0 missing
C.032numeric2 unique values
0 missing
ATSC7enumeric369 unique values
0 missing
Eig02_EA.bo.numeric308 unique values
0 missing
SM12_AEA.ri.numeric308 unique values
0 missing
ZM1MulPernumeric611 unique values
0 missing
ZM1Vnumeric208 unique values
0 missing
Eig08_AEA.dm.numeric484 unique values
0 missing
Eig03_EA.ed.numeric395 unique values
0 missing
SM12_AEA.dm.numeric395 unique values
0 missing
ATSC8enumeric372 unique values
0 missing
nABnumeric15 unique values
0 missing
Psi_i_snumeric333 unique values
0 missing
SAdonnumeric52 unique values
0 missing
Eig03_AEA.bo.numeric401 unique values
0 missing
C.027numeric5 unique values
0 missing
ATSC5enumeric419 unique values
0 missing
ATSC4enumeric408 unique values
0 missing
ATS1snumeric476 unique values
0 missing
Eig14_AEA.dm.numeric303 unique values
0 missing
Eig02_AEA.bo.numeric343 unique values
0 missing
nNnumeric9 unique values
0 missing
P_VSA_LogP_4numeric123 unique values
0 missing
GMTIVnumeric624 unique values
0 missing
CATS2D_02_AAnumeric9 unique values
0 missing
Eig14_EAnumeric260 unique values
0 missing
Eig14_EA.bo.numeric272 unique values
0 missing
Eig14_EA.ed.numeric289 unique values
0 missing
Eig14_EA.ri.numeric338 unique values
0 missing
SM08_AEA.dm.numeric260 unique values
0 missing
SM09_AEA.ri.numeric289 unique values
0 missing
IDDMnumeric266 unique values
0 missing
H.048numeric5 unique values
0 missing
SpMax2_Bh.i.numeric359 unique values
0 missing
Eig09_AEA.dm.numeric445 unique values
0 missing
IDMnumeric411 unique values
0 missing
Dznumeric167 unique values
0 missing
Eig10_AEA.dm.numeric424 unique values
0 missing
Eig15_AEA.dm.numeric298 unique values
0 missing
H.050numeric11 unique values
0 missing
nHDonnumeric11 unique values
0 missing
MATS1mnumeric155 unique values
0 missing
CATS2D_09_ALnumeric9 unique values
0 missing
Eig14_AEA.ed.numeric267 unique values
0 missing
Eig03_EA.bo.numeric399 unique values
0 missing
SM13_AEA.ri.numeric399 unique values
0 missing
X0numeric207 unique values
0 missing
ATS2snumeric502 unique values
0 missing
Eta_betaSnumeric82 unique values
0 missing
nBMnumeric25 unique values
0 missing
Ucnumeric25 unique values
0 missing
DELSnumeric666 unique values
0 missing
C.007numeric2 unique values
0 missing
SaasNnumeric107 unique values
0 missing
CATS2D_04_DLnumeric10 unique values
0 missing

62 properties

713
Number of instances (rows) of the dataset.
70
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.
69
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.
0.1
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.67
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.
0.47
Mean skewness among attributes of the numeric type.
1.14
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
200.13
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.
0.16
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.63
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
13.44
Maximum kurtosis among attributes of the numeric type.
-1
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
13948.96
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.
2.33
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.
98.57
Percentage of numeric attributes.
4.83
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-1.44
Minimum skewness among attributes of the numeric type.
1.43
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
3.92
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.55
Third quartile of skewness among attributes of the numeric type.
13344.84
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.52
First quartile of kurtosis among attributes of the numeric type.
2.02
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.
0.23
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
1.4
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.
214.94
Mean of means among attributes of the numeric type.
-0.71
First quartile of skewness among attributes of the numeric type.
0.34
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.57
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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