Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4554

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4554

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: CHEMBL4554 (TID: 10981), and it has 262 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)numeric150 unique values
0 missing
molecule_id (row identifier)nominal262 unique values
0 missing
Eig02_EA.dm.numeric58 unique values
0 missing
CATS2D_09_APnumeric7 unique values
0 missing
SM05_EA.dm.numeric97 unique values
0 missing
SM07_EA.dm.numeric104 unique values
0 missing
LOCnumeric172 unique values
0 missing
H.052numeric29 unique values
0 missing
CATS2D_09_PLnumeric10 unique values
0 missing
MCDnumeric147 unique values
0 missing
C.001numeric8 unique values
0 missing
CATS2D_08_DPnumeric5 unique values
0 missing
CATS2D_07_ALnumeric37 unique values
0 missing
Eig04_AEA.dm.numeric141 unique values
0 missing
Eig04_EA.dm.numeric69 unique values
0 missing
GATS2enumeric141 unique values
0 missing
SM09_EA.dm.numeric97 unique values
0 missing
SM11_EA.dm.numeric101 unique values
0 missing
SM13_EA.dm.numeric99 unique values
0 missing
CATS2D_08_AAnumeric15 unique values
0 missing
P_VSA_MR_2numeric120 unique values
0 missing
SM03_EA.dm.numeric75 unique values
0 missing
nCpnumeric10 unique values
0 missing
Eig06_AEA.dm.numeric137 unique values
0 missing
CATS2D_05_AAnumeric15 unique values
0 missing
P_VSA_LogP_2numeric146 unique values
0 missing
P_VSA_LogP_1numeric27 unique values
0 missing
Eig05_EA.dm.numeric62 unique values
0 missing
CATS2D_06_DAnumeric17 unique values
0 missing
CATS2D_08_PLnumeric9 unique values
0 missing
NdssCnumeric15 unique values
0 missing
SM15_EA.dm.numeric96 unique values
0 missing
P_VSA_LogP_4numeric67 unique values
0 missing
SpMax6_Bh.s.numeric74 unique values
0 missing
GATS7inumeric153 unique values
0 missing
C.040numeric15 unique values
0 missing
BACnumeric111 unique values
0 missing
H.046numeric16 unique values
0 missing
CATS2D_04_ALnumeric29 unique values
0 missing
GGI5numeric173 unique values
0 missing
MATS2enumeric133 unique values
0 missing
MATS4enumeric132 unique values
0 missing
ON0numeric155 unique values
0 missing
SPInumeric201 unique values
0 missing
GGI4numeric176 unique values
0 missing
SsNH2numeric128 unique values
0 missing
MATS4snumeric120 unique values
0 missing
GGI3numeric152 unique values
0 missing
Eig02_AEA.dm.numeric112 unique values
0 missing
SdOnumeric227 unique values
0 missing
CATS2D_00_DDnumeric7 unique values
0 missing
CATS2D_00_DPnumeric7 unique values
0 missing
CATS2D_00_PPnumeric7 unique values
0 missing
NsNH2numeric7 unique values
0 missing
Eig08_EA.dm.numeric62 unique values
0 missing
SsCH3numeric178 unique values
0 missing
MATS7inumeric137 unique values
0 missing
N.072numeric14 unique values
0 missing
SpAD_EA.dm.numeric144 unique values
0 missing
S1Knumeric200 unique values
0 missing
DLS_07numeric3 unique values
0 missing
CATS2D_06_ALnumeric33 unique values
0 missing
Eig09_EA.dm.numeric60 unique values
0 missing
Eig11_AEA.dm.numeric131 unique values
0 missing
DLS_consnumeric33 unique values
0 missing
DBInumeric113 unique values
0 missing

107 properties

262
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
340.53
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.34
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
0.25
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.
2.16
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
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
1.92
Maximum skewness among attributes of the numeric type.
0.05
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.89
Third quartile of kurtosis among attributes of the numeric type.
-0.33
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
213.3
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.48
Percentage of numeric attributes.
10.08
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
0.6
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.
0.86
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
21.72
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.68
First quartile of kurtosis among attributes of the numeric type.
4.59
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
1.66
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.
-0.24
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
0.15
Mean skewness among attributes of the numeric type.
0.76
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.
13.71
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.
-0.1
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.
-1.83
Minimum kurtosis among attributes of the numeric type.
4.18
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
7.04
Maximum kurtosis among attributes of the numeric type.
-0.14
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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