Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2074

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2074

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: CHEMBL2074 (TID: 1), and it has 103 rows and 61 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.

63 features

pXC50 (target)numeric76 unique values
0 missing
molecule_id (row identifier)nominal103 unique values
0 missing
P_VSA_e_5numeric28 unique values
0 missing
P_VSA_LogP_3numeric43 unique values
0 missing
P_VSA_m_3numeric28 unique values
0 missing
P_VSA_p_2numeric41 unique values
0 missing
P_VSA_s_6numeric33 unique values
0 missing
P_VSA_v_2numeric41 unique values
0 missing
SAaccnumeric41 unique values
0 missing
ATSC3enumeric80 unique values
0 missing
P_VSA_MR_3numeric21 unique values
0 missing
nOHsnumeric9 unique values
0 missing
MAXDNnumeric74 unique values
0 missing
SdssCnumeric27 unique values
0 missing
C.017numeric4 unique values
0 missing
nR.Ctnumeric4 unique values
0 missing
ATSC3snumeric83 unique values
0 missing
SsssCHnumeric79 unique values
0 missing
SAdonnumeric23 unique values
0 missing
SsOHnumeric77 unique values
0 missing
MATS1vnumeric62 unique values
0 missing
nHetnumeric12 unique values
0 missing
MATS1inumeric70 unique values
0 missing
ATSC4enumeric81 unique values
0 missing
CATS2D_06_DDnumeric7 unique values
0 missing
nROHnumeric11 unique values
0 missing
O.056numeric11 unique values
0 missing
MATS5mnumeric65 unique values
0 missing
NdssCnumeric7 unique values
0 missing
MATS5pnumeric72 unique values
0 missing
Eta_F_Anumeric77 unique values
0 missing
Eta_FL_Anumeric62 unique values
0 missing
Eta_L_Anumeric59 unique values
0 missing
NaaCHnumeric10 unique values
0 missing
nABnumeric6 unique values
0 missing
nCarnumeric8 unique values
0 missing
P_VSA_LogP_4numeric11 unique values
0 missing
P_VSA_MR_2numeric18 unique values
0 missing
GATS8inumeric68 unique values
0 missing
C.008numeric10 unique values
0 missing
CATS2D_04_DDnumeric8 unique values
0 missing
BLTA96numeric69 unique values
0 missing
BLTD48numeric69 unique values
0 missing
BLTF96numeric69 unique values
0 missing
MLOGPnumeric69 unique values
0 missing
PDInumeric63 unique values
0 missing
CATS2D_03_DDnumeric9 unique values
0 missing
MATS8inumeric77 unique values
0 missing
MATS5enumeric68 unique values
0 missing
GATS8enumeric72 unique values
0 missing
X3Anumeric33 unique values
0 missing
MATS1pnumeric72 unique values
0 missing
NsOHnumeric11 unique values
0 missing
JGI7numeric16 unique values
0 missing
Eig15_EAnumeric51 unique values
0 missing
Eig15_EA.bo.numeric60 unique values
0 missing
Eig15_EA.ed.numeric55 unique values
0 missing
Eig15_EA.ri.numeric56 unique values
0 missing
SM09_AEA.dm.numeric51 unique values
0 missing
SM10_AEA.ri.numeric55 unique values
0 missing
GATS8pnumeric71 unique values
0 missing
MATS5snumeric64 unique values
0 missing
SpMin5_Bh.e.numeric71 unique values
0 missing

107 properties

103
Number of instances (rows) of the dataset.
63
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.
62
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
246.09
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.25
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
0.61
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.
1.8
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.
-1.48
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
2.56
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.83
Third quartile of kurtosis among attributes of the numeric type.
-0.19
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
104.23
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.41
Percentage of numeric attributes.
5.19
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.
1.59
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
0.62
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.
0.89
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
39.24
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.27
First quartile of kurtosis among attributes of the numeric type.
4.17
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
0.01
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.
-0.23
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
0.42
Mean skewness among attributes of the numeric type.
0.39
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.
17.48
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.
-0.12
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.
-1.15
Minimum kurtosis among attributes of the numeric type.
0.92
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
5.73
Maximum kurtosis among attributes of the numeric type.
-5.52
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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