Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4715

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4715

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: CHEMBL4715 (TID: 10013), and it has 101 rows and 62 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.

64 features

pXC50 (target)numeric51 unique values
0 missing
molecule_id (row identifier)nominal101 unique values
0 missing
Yindexnumeric84 unique values
0 missing
MATS1enumeric70 unique values
0 missing
P_VSA_LogP_5numeric32 unique values
0 missing
Vindexnumeric68 unique values
0 missing
MATS1snumeric69 unique values
0 missing
Xindexnumeric74 unique values
0 missing
SAdonnumeric14 unique values
0 missing
HNarnumeric52 unique values
0 missing
Hynumeric66 unique values
0 missing
Eta_sh_pnumeric66 unique values
0 missing
ATSC7snumeric98 unique values
0 missing
MAXDNnumeric72 unique values
0 missing
CATS2D_09_DLnumeric6 unique values
0 missing
SpMax1_Bh.m.numeric57 unique values
0 missing
SpMaxA_AEA.ri.numeric50 unique values
0 missing
CATS2D_03_DAnumeric4 unique values
0 missing
X0Anumeric33 unique values
0 missing
CATS2D_08_DLnumeric8 unique values
0 missing
nROHnumeric3 unique values
0 missing
NsOHnumeric3 unique values
0 missing
SsOHnumeric21 unique values
0 missing
Eig04_EA.dm.numeric15 unique values
0 missing
ATSC2snumeric99 unique values
0 missing
CATS2D_06_ANnumeric2 unique values
0 missing
SpMaxA_EA.ri.numeric45 unique values
0 missing
CATS2D_01_ANnumeric4 unique values
0 missing
CATS2D_01_DNnumeric3 unique values
0 missing
CATS2D_06_NLnumeric4 unique values
0 missing
CATS2D_07_NLnumeric4 unique values
0 missing
CATS2D_08_NLnumeric4 unique values
0 missing
CATS2D_09_NLnumeric4 unique values
0 missing
SM10_EA.ri.numeric97 unique values
0 missing
SM11_EA.ri.numeric68 unique values
0 missing
SM12_EA.ri.numeric99 unique values
0 missing
Eig15_AEA.ed.numeric76 unique values
0 missing
ATSC1snumeric91 unique values
0 missing
Chi0_EA.ed.numeric95 unique values
0 missing
SpMaxA_EAnumeric49 unique values
0 missing
CATS2D_01_AAnumeric3 unique values
0 missing
D.Dtr05numeric73 unique values
0 missing
NdsNnumeric3 unique values
0 missing
nN.C.N.numeric3 unique values
0 missing
nROCONnumeric3 unique values
0 missing
SdsNnumeric56 unique values
0 missing
SM07_EA.dm.numeric33 unique values
0 missing
SM09_EA.dm.numeric29 unique values
0 missing
SM11_EA.dm.numeric26 unique values
0 missing
SM13_EA.dm.numeric27 unique values
0 missing
SM15_EA.dm.numeric25 unique values
0 missing
BLTA96numeric75 unique values
0 missing
BLTD48numeric75 unique values
0 missing
BLTF96numeric78 unique values
0 missing
MLOGPnumeric81 unique values
0 missing
MLOGP2numeric81 unique values
0 missing
CATS2D_07_DLnumeric8 unique values
0 missing
Eig12_EA.ed.numeric62 unique values
0 missing
Eig13_AEA.bo.numeric72 unique values
0 missing
Eig13_AEA.ri.numeric69 unique values
0 missing
Eig13_EAnumeric68 unique values
0 missing
Eig13_EA.bo.numeric73 unique values
0 missing
Eig13_EA.ed.numeric66 unique values
0 missing
Eig13_EA.ri.numeric68 unique values
0 missing

107 properties

101
Number of instances (rows) of the dataset.
64
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.
63
Number of numeric attributes.
1
Number of nominal attributes.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
112.17
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.22
Second quartile (Median) of skewness among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.63
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.7
Second quartile (Median) of standard deviation of attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
The maximum number of distinct values among attributes of the nominal type.
-2.02
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.72
Maximum skewness among attributes of the numeric type.
0.02
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
2.64
Third quartile of kurtosis among attributes of the numeric type.
0.56
Average class difference between consecutive instances.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
80.77
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.44
Percentage of numeric attributes.
5.52
Third quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
1.56
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.11
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.8
Third quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
7.57
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.93
First quartile of kurtosis among attributes of the numeric type.
1.69
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.27
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Average number of distinct values among the attributes of the nominal type.
-0.95
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.59
Mean skewness among attributes of the numeric type.
0.43
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
4.19
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
2.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.68
Minimum kurtosis among attributes of the numeric type.
0.95
Second quartile (Median) of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
8.29
Maximum kurtosis among attributes of the numeric type.
-6.4
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

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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