Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5631

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5631

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: CHEMBL5631 (TID: 101488), and it has 122 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)numeric112 unique values
0 missing
molecule_id (row identifier)nominal122 unique values
0 missing
CATS2D_08_DAnumeric6 unique values
0 missing
P_VSA_s_5numeric11 unique values
0 missing
GGI9numeric57 unique values
0 missing
ATSC8snumeric116 unique values
0 missing
SpDiam_AEA.bo.numeric76 unique values
0 missing
CATS2D_04_DLnumeric11 unique values
0 missing
NaaNHnumeric2 unique values
0 missing
nPyrrolesnumeric2 unique values
0 missing
SaaNHnumeric18 unique values
0 missing
SpMax1_Bh.i.numeric71 unique values
0 missing
CATS2D_03_DLnumeric11 unique values
0 missing
Eig04_AEA.dm.numeric98 unique values
0 missing
CATS2D_08_APnumeric4 unique values
0 missing
CATS2D_08_AAnumeric6 unique values
0 missing
CATS2D_07_LLnumeric14 unique values
0 missing
P_VSA_MR_5numeric58 unique values
0 missing
SpMax8_Bh.s.numeric95 unique values
0 missing
CATS2D_08_DPnumeric3 unique values
0 missing
C.034numeric3 unique values
0 missing
CATS2D_06_DLnumeric10 unique values
0 missing
CATS2D_06_PLnumeric6 unique values
0 missing
SpMax1_Bh.e.numeric66 unique values
0 missing
SpMax1_Bh.v.numeric67 unique values
0 missing
SpMax2_Bh.i.numeric88 unique values
0 missing
GGI6numeric64 unique values
0 missing
GGI2numeric20 unique values
0 missing
SM06_AEA.bo.numeric90 unique values
0 missing
nCsp2numeric16 unique values
0 missing
Psi_i_snumeric101 unique values
0 missing
Eig08_EA.bo.numeric61 unique values
0 missing
ATS4snumeric117 unique values
0 missing
CATS2D_08_DDnumeric3 unique values
0 missing
Eig05_AEA.dm.numeric106 unique values
0 missing
SM05_EA.ri.numeric107 unique values
0 missing
GMTIVnumeric108 unique values
0 missing
piPC05numeric91 unique values
0 missing
piPC06numeric88 unique values
0 missing
piPC07numeric90 unique values
0 missing
SM04_EA.bo.numeric88 unique values
0 missing
SM06_EA.bo.numeric93 unique values
0 missing
SM07_AEA.bo.numeric91 unique values
0 missing
SM08_AEA.bo.numeric93 unique values
0 missing
Eig03_AEA.dm.numeric105 unique values
0 missing
ATSC8enumeric109 unique values
0 missing
ZM1Kupnumeric104 unique values
0 missing
CATS2D_05_DLnumeric10 unique values
0 missing
ATS6snumeric117 unique values
0 missing
SssNHnumeric68 unique values
0 missing
ATS5snumeric112 unique values
0 missing
ZM1MulPernumeric108 unique values
0 missing
Eig02_AEA.dm.numeric79 unique values
0 missing
P_VSA_m_2numeric104 unique values
0 missing
C.028numeric3 unique values
0 missing
CATS2D_01_DPnumeric2 unique values
0 missing
CATS2D_02_PLnumeric4 unique values
0 missing
CATS2D_09_PPnumeric2 unique values
0 missing
Eig14_AEA.ri.numeric49 unique values
0 missing
Eig14_EAnumeric41 unique values
0 missing
Eig14_EA.bo.numeric66 unique values
0 missing
Eig14_EA.ed.numeric48 unique values
0 missing
Eig14_EA.ri.numeric47 unique values
0 missing
H.051numeric4 unique values
0 missing
nABnumeric10 unique values
0 missing
nCarnumeric13 unique values
0 missing
nN.numeric2 unique values
0 missing

62 properties

122
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.
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.
0.55
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.98
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.
0.41
Mean skewness among attributes of the numeric type.
3.68
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
210.25
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.
0.41
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.28
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.74
Second quartile (Median) of standard deviation of attributes of the numeric type.
7.3
Maximum kurtosis among attributes of the numeric type.
-1.73
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
18370.84
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.
2.5
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.
98.51
Percentage of numeric attributes.
6.34
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-1.93
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.
2.33
Maximum skewness among attributes of the numeric type.
0.08
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.19
Third quartile of skewness among attributes of the numeric type.
13362.69
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.46
First quartile of kurtosis among attributes of the numeric type.
2.16
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.
0.45
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
1.35
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.
298.3
Mean of means among attributes of the numeric type.
-0.26
First quartile of skewness among attributes of the numeric type.
0.18
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.48
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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