Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3272

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3272

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: CHEMBL3272 (TID: 12587), and it has 157 rows and 64 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.

66 features

pXC50 (target)numeric101 unique values
0 missing
molecule_id (row identifier)nominal157 unique values
0 missing
SpDiam_AEA.ed.numeric94 unique values
0 missing
SpMax2_Bh.i.numeric97 unique values
0 missing
Eig03_AEA.dm.numeric105 unique values
0 missing
CATS2D_03_DAnumeric8 unique values
0 missing
NssNHnumeric6 unique values
0 missing
P_VSA_s_5numeric15 unique values
0 missing
SssNHnumeric101 unique values
0 missing
JGI1numeric81 unique values
0 missing
SRW07numeric16 unique values
0 missing
SRW09numeric35 unique values
0 missing
SsssCHnumeric95 unique values
0 missing
SpMin1_Bh.p.numeric80 unique values
0 missing
IVDEnumeric93 unique values
0 missing
C.008numeric7 unique values
0 missing
P_VSA_e_3numeric47 unique values
0 missing
nRCONHRnumeric5 unique values
0 missing
DBInumeric57 unique values
0 missing
TPSA.NO.numeric72 unique values
0 missing
N.072numeric7 unique values
0 missing
P_VSA_m_2numeric137 unique values
0 missing
TPSA.Tot.numeric76 unique values
0 missing
Eig02_AEA.dm.numeric92 unique values
0 missing
P_VSA_LogP_2numeric54 unique values
0 missing
Eig04_AEA.dm.numeric98 unique values
0 missing
SpMax2_Bh.e.numeric87 unique values
0 missing
Eta_sh_xnumeric32 unique values
0 missing
CATS2D_02_DAnumeric6 unique values
0 missing
C.040numeric7 unique values
0 missing
Eig08_AEA.bo.numeric111 unique values
0 missing
MCDnumeric83 unique values
0 missing
Eig05_AEA.dm.numeric122 unique values
0 missing
O.058numeric9 unique values
0 missing
Eig01_AEA.dm.numeric56 unique values
0 missing
SpMax_AEA.dm.numeric56 unique values
0 missing
MATS6pnumeric131 unique values
0 missing
Eig03_EA.ri.numeric110 unique values
0 missing
SpDiam_AEA.dm.numeric57 unique values
0 missing
nPyrazolesnumeric3 unique values
0 missing
N.numeric67 unique values
0 missing
NsssCHnumeric10 unique values
0 missing
C.028numeric5 unique values
0 missing
GATS5mnumeric125 unique values
0 missing
PCDnumeric124 unique values
0 missing
MATS5mnumeric125 unique values
0 missing
MATS1snumeric99 unique values
0 missing
TPCnumeric114 unique values
0 missing
SsssNnumeric37 unique values
0 missing
Eig07_AEA.ed.numeric113 unique values
0 missing
O.057numeric4 unique values
0 missing
GATS5snumeric133 unique values
0 missing
Eig12_EA.bo.numeric98 unique values
0 missing
ATSC4enumeric147 unique values
0 missing
nRCNnumeric2 unique values
0 missing
CATS2D_07_ALnumeric20 unique values
0 missing
MATS3vnumeric119 unique values
0 missing
Eig11_AEA.bo.numeric98 unique values
0 missing
SM06_AEA.bo.numeric121 unique values
0 missing
piPC06numeric123 unique values
0 missing
piPC08numeric121 unique values
0 missing
CATS2D_06_ALnumeric18 unique values
0 missing
SpMax1_Bh.e.numeric102 unique values
0 missing
piPC07numeric123 unique values
0 missing
piPC10numeric120 unique values
0 missing
MATS2snumeric120 unique values
0 missing

107 properties

157
Number of instances (rows) of the dataset.
66
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.
65
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
185.9
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.14
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
0.42
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.
0.71
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.
-5.85
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
4.1
Maximum skewness among attributes of the numeric type.
0.02
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
4.17
Third quartile of kurtosis among attributes of the numeric type.
-0.01
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
61.27
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.48
Percentage of numeric attributes.
5.93
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.
1.52
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
4.59
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.
1.18
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
10.15
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.08
First quartile of kurtosis among attributes of the numeric type.
1.42
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
0.71
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.
-0.51
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
0.22
Mean skewness among attributes of the numeric type.
0.32
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.
4.29
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.
1.04
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.
-1.97
Minimum kurtosis among attributes of the numeric type.
2.6
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
46.09
Maximum kurtosis among attributes of the numeric type.
-0.9
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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