Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3710

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3710

deactivated ARFF Publicly available Visibility: public Uploaded 14-07-2016 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: CHEMBL3710 (TID: 10329), and it has 669 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)numeric415 unique values
0 missing
molecule_id (row identifier)nominal669 unique values
0 missing
SpMin3_Bh.s.numeric254 unique values
0 missing
SpMin2_Bh.s.numeric207 unique values
0 missing
P_VSA_MR_2numeric132 unique values
0 missing
nCsp3numeric21 unique values
0 missing
nCsnumeric17 unique values
0 missing
NssCH2numeric15 unique values
0 missing
DLS_05numeric3 unique values
0 missing
SpMAD_EA.bo.numeric283 unique values
0 missing
PW5numeric54 unique values
0 missing
PDInumeric167 unique values
0 missing
Eta_beta_Anumeric256 unique values
0 missing
Eta_betaP_Anumeric206 unique values
0 missing
C.008numeric4 unique values
0 missing
P_VSA_s_2numeric60 unique values
0 missing
Eig02_AEA.bo.numeric245 unique values
0 missing
ATSC2vnumeric527 unique values
0 missing
nHnumeric34 unique values
0 missing
SpMin5_Bh.s.numeric294 unique values
0 missing
SpMin4_Bh.s.numeric287 unique values
0 missing
NsssCHnumeric6 unique values
0 missing
nOHsnumeric4 unique values
0 missing
GATS2vnumeric322 unique values
0 missing
ON1Vnumeric440 unique values
0 missing
P_VSA_p_1numeric95 unique values
0 missing
O.056numeric5 unique values
0 missing
ATSC1vnumeric493 unique values
0 missing
P_VSA_e_1numeric34 unique values
0 missing
P_VSA_m_1numeric32 unique values
0 missing
P_VSA_v_1numeric32 unique values
0 missing
P_VSA_LogP_7numeric88 unique values
0 missing
ATSC3vnumeric598 unique values
0 missing
Eta_FL_Anumeric141 unique values
0 missing
ATS3inumeric401 unique values
0 missing
C.002numeric14 unique values
0 missing
ATSC1mnumeric511 unique values
0 missing
P_VSA_MR_3numeric12 unique values
0 missing
SsOHnumeric185 unique values
0 missing
piPC09numeric451 unique values
0 missing
CMC.80numeric2 unique values
0 missing
SpMax1_Bh.v.numeric230 unique values
0 missing
SsssNnumeric213 unique values
0 missing
Eig08_EA.bo.numeric343 unique values
0 missing
nROHnumeric5 unique values
0 missing
nArXnumeric7 unique values
0 missing
P_VSA_i_3numeric361 unique values
0 missing
Eta_F_Anumeric391 unique values
0 missing
D.Dtr09numeric239 unique values
0 missing
NsOHnumeric5 unique values
0 missing
ZM1MulPernumeric582 unique values
0 missing
nBMnumeric34 unique values
0 missing
Ucnumeric34 unique values
0 missing
Eig07_EA.bo.numeric343 unique values
0 missing
ATS7inumeric474 unique values
0 missing
DLS_01numeric4 unique values
0 missing
SpDiam_EA.bo.numeric214 unique values
0 missing
Eig09_AEA.bo.numeric302 unique values
0 missing
P_VSA_m_4numeric51 unique values
0 missing
ATSC4vnumeric632 unique values
0 missing
RBFnumeric128 unique values
0 missing
ATS3mnumeric464 unique values
0 missing
ZM1Vnumeric232 unique values
0 missing
ATS2mnumeric415 unique values
0 missing
ZM2Madnumeric583 unique values
0 missing
nABnumeric24 unique values
0 missing
CATS2D_02_DAnumeric6 unique values
0 missing
DLS_consnumeric46 unique values
0 missing

62 properties

669
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.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.88
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
1.12
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.
54.22
Mean of means among attributes of the numeric type.
-0.37
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.22
First quartile of standard deviation of attributes of the numeric type.
0.03
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.
0.14
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.1
Number of attributes divided by the number of instances.
0.37
Mean skewness among attributes of the numeric type.
4.01
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.
17.98
Mean standard deviation of 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.
Minimal entropy among attributes.
0.39
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
-1.76
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.97
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
10.44
Maximum kurtosis among attributes of the numeric type.
0.1
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
594.78
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.
1.81
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.
13.76
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-2.01
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.
3.31
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.
0.87
Third quartile of skewness among attributes of the numeric type.
170.35
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.27
First quartile of kurtosis among attributes of the numeric type.
7.04
Third 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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