Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4566

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4566

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: CHEMBL4566 (TID: 12569), and it has 891 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)numeric471 unique values
0 missing
molecule_id (row identifier)nominal891 unique values
0 missing
LOCnumeric358 unique values
0 missing
CATS2D_04_NLnumeric8 unique values
0 missing
CATS2D_06_ANnumeric6 unique values
0 missing
GATS1vnumeric328 unique values
0 missing
NssOnumeric8 unique values
0 missing
nArORnumeric8 unique values
0 missing
CATS2D_03_NLnumeric5 unique values
0 missing
GATS2enumeric443 unique values
0 missing
CATS2D_07_AAnumeric10 unique values
0 missing
O.060numeric8 unique values
0 missing
CATS2D_03_DAnumeric13 unique values
0 missing
SsOHnumeric480 unique values
0 missing
GATS4enumeric453 unique values
0 missing
nCrtnumeric5 unique values
0 missing
nRNR2numeric3 unique values
0 missing
CATS2D_08_ALnumeric38 unique values
0 missing
N.numeric88 unique values
0 missing
PCRnumeric286 unique values
0 missing
P_VSA_e_3numeric102 unique values
0 missing
C.006numeric10 unique values
0 missing
Eig09_AEA.bo.numeric357 unique values
0 missing
N.068numeric3 unique values
0 missing
MATS2enumeric350 unique values
0 missing
Eig01_EA.ed.numeric272 unique values
0 missing
SM10_AEA.dm.numeric272 unique values
0 missing
SpMax_EA.ed.numeric272 unique values
0 missing
P_VSA_s_5numeric39 unique values
0 missing
Eig10_EA.bo.numeric353 unique values
0 missing
nPyrrolidinesnumeric2 unique values
0 missing
Eig08_EA.bo.numeric391 unique values
0 missing
Eig08_AEA.bo.numeric364 unique values
0 missing
Eig04_EA.dm.numeric46 unique values
0 missing
C.007numeric3 unique values
0 missing
CATS2D_01_NLnumeric6 unique values
0 missing
Eta_Fnumeric851 unique values
0 missing
CATS2D_04_LLnumeric27 unique values
0 missing
SM03_AEA.bo.numeric377 unique values
0 missing
ARRnumeric149 unique values
0 missing
CATS2D_02_DAnumeric19 unique values
0 missing
SpMin2_Bh.p.numeric197 unique values
0 missing
MATS3pnumeric276 unique values
0 missing
SssOnumeric569 unique values
0 missing
Xindexnumeric200 unique values
0 missing
SpMin2_Bh.v.numeric192 unique values
0 missing
Eig11_AEA.dm.numeric477 unique values
0 missing
ZM2Kupnumeric761 unique values
0 missing
Vindexnumeric154 unique values
0 missing
Eig02_AEA.dm.numeric295 unique values
0 missing
ZM1Pernumeric808 unique values
0 missing
NssNHnumeric14 unique values
0 missing
Hypnotic.80numeric2 unique values
0 missing
P_VSA_e_2numeric692 unique values
0 missing
IC4numeric495 unique values
0 missing
CATS2D_07_DAnumeric13 unique values
0 missing
SpMax4_Bh.m.numeric383 unique values
0 missing
MATS1inumeric314 unique values
0 missing
Chi1_EA.ed.numeric587 unique values
0 missing
P_VSA_LogP_5numeric336 unique values
0 missing
ZM1MulPernumeric813 unique values
0 missing
P_VSA_i_2numeric688 unique values
0 missing
P_VSA_p_3numeric691 unique values
0 missing
P_VSA_v_3numeric691 unique values
0 missing
RBFnumeric112 unique values
0 missing
SpMax8_Bh.p.numeric391 unique values
0 missing
H.numeric168 unique values
0 missing
BIC0numeric134 unique values
0 missing
DLS_04numeric9 unique values
0 missing
SpMin2_Bh.i.numeric204 unique values
0 missing

62 properties

891
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.
0.08
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
1.94
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.
1.2
Mean skewness among attributes of the numeric type.
1.98
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
24.96
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.
0.64
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.47
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.84
Second quartile (Median) of standard deviation of attributes of the numeric type.
29.45
Maximum kurtosis among attributes of the numeric type.
-0.09
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
817.42
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.
8.63
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.
9.59
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-1.94
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.
5.38
Maximum skewness among attributes of the numeric type.
0.03
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
2.63
Third quartile of skewness among attributes of the numeric type.
475.83
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.19
First quartile of kurtosis among attributes of the numeric type.
4.01
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.
0.76
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
6.23
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.
43.98
Mean of means among attributes of the numeric type.
-0.06
First quartile of skewness among attributes of the numeric type.
-0.4
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.23
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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