Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5398

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5398

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: CHEMBL5398 (TID: 101173), and it has 108 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)numeric54 unique values
0 missing
molecule_id (row identifier)nominal108 unique values
0 missing
P_VSA_MR_1numeric34 unique values
0 missing
ATSC1vnumeric103 unique values
0 missing
ATSC3vnumeric105 unique values
0 missing
ATSC4vnumeric104 unique values
0 missing
Minumeric40 unique values
0 missing
SpMin7_Bh.p.numeric80 unique values
0 missing
SpMin7_Bh.v.numeric81 unique values
0 missing
C.numeric67 unique values
0 missing
GATS1vnumeric95 unique values
0 missing
CIC0numeric98 unique values
0 missing
SpMin5_Bh.s.numeric89 unique values
0 missing
Mvnumeric78 unique values
0 missing
SpMin4_Bh.s.numeric90 unique values
0 missing
SpMin6_Bh.s.numeric75 unique values
0 missing
ATSC1inumeric96 unique values
0 missing
ATSC5vnumeric106 unique values
0 missing
SpMAD_EA.ri.numeric87 unique values
0 missing
SpMax6_Bh.i.numeric88 unique values
0 missing
SpMin6_Bh.e.numeric87 unique values
0 missing
SpMin6_Bh.i.numeric84 unique values
0 missing
SpMin6_Bh.v.numeric85 unique values
0 missing
DLS_05numeric3 unique values
0 missing
nHnumeric30 unique values
0 missing
P_VSA_e_1numeric32 unique values
0 missing
P_VSA_m_1numeric32 unique values
0 missing
P_VSA_p_1numeric40 unique values
0 missing
P_VSA_s_2numeric35 unique values
0 missing
P_VSA_v_1numeric32 unique values
0 missing
P_VSA_LogP_2numeric78 unique values
0 missing
P_VSA_m_2numeric104 unique values
0 missing
PHInumeric103 unique values
0 missing
S3Knumeric104 unique values
0 missing
SpMax3_Bh.i.numeric96 unique values
0 missing
SpMin7_Bh.e.numeric87 unique values
0 missing
SpMin7_Bh.i.numeric82 unique values
0 missing
nCsp3numeric21 unique values
0 missing
Eta_FL_Anumeric78 unique values
0 missing
ATS4inumeric99 unique values
0 missing
ATSC1enumeric78 unique values
0 missing
RCInumeric27 unique values
0 missing
X3Avnumeric50 unique values
0 missing
GATS1inumeric90 unique values
0 missing
SpMaxA_EA.ed.numeric86 unique values
0 missing
Eta_beta_Anumeric95 unique values
0 missing
SpMax5_Bh.v.numeric92 unique values
0 missing
MATS2mnumeric85 unique values
0 missing
ATS8mnumeric105 unique values
0 missing
ATS8vnumeric104 unique values
0 missing
MSDnumeric100 unique values
0 missing
P_VSA_MR_5numeric96 unique values
0 missing
SpMAD_EA.bo.numeric86 unique values
0 missing
SpMax6_Bh.e.numeric87 unique values
0 missing
SpMax8_Bh.i.numeric82 unique values
0 missing
SpMax8_Bh.m.numeric85 unique values
0 missing
SpMin2_Bh.m.numeric83 unique values
0 missing
SpMin2_Bh.s.numeric88 unique values
0 missing
ATSC1snumeric105 unique values
0 missing
X2Avnumeric70 unique values
0 missing
MATS1inumeric89 unique values
0 missing
P_VSA_e_2numeric104 unique values
0 missing
P_VSA_i_2numeric104 unique values
0 missing
ATS8snumeric105 unique values
0 missing
ATS5snumeric103 unique values
0 missing
P_VSA_p_3numeric104 unique values
0 missing
P_VSA_v_3numeric104 unique values
0 missing
AACnumeric93 unique values
0 missing

62 properties

108
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.
0.63
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.9
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.
-0.01
Mean skewness among attributes of the numeric type.
3.19
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
13.55
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.
0.06
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
28.04
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.
258.41
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.
2.67
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.
14.17
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-3.67
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.
3.28
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.
0.67
Third quartile of skewness among attributes of the numeric type.
97.3
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.45
First quartile of kurtosis among attributes of the numeric type.
6.52
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.
1.11
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
2.29
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.
37.61
Mean of means among attributes of the numeric type.
-0.98
First quartile of skewness among attributes of the numeric type.
0.76
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.17
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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