Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3455

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3455

deactivated ARFF Publicly available Visibility: public Uploaded 14-07-2016 by Noureddin Sadawi
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
This dataset contains QSAR data (from ChEMBL version 17) showing activity values (unit is pseudo-pCI50) of several compounds on drug target ChEMBL_ID: CHEMBL3455 (TID: 12411), and it has 147 rows and 63 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.

65 features

pXC50 (target)numeric102 unique values
0 missing
molecule_id (row identifier)nominal147 unique values
0 missing
P_VSA_LogP_2numeric53 unique values
0 missing
BIC5numeric93 unique values
0 missing
SdOnumeric141 unique values
0 missing
P_VSA_MR_5numeric94 unique values
0 missing
X5Anumeric39 unique values
0 missing
ATS6snumeric141 unique values
0 missing
ATS6mnumeric130 unique values
0 missing
CATS2D_04_ALnumeric17 unique values
0 missing
X5solnumeric119 unique values
0 missing
SpMin3_Bh.e.numeric87 unique values
0 missing
ATS5vnumeric136 unique values
0 missing
ATS6vnumeric139 unique values
0 missing
SpMin3_Bh.i.numeric97 unique values
0 missing
Eig13_AEA.ri.numeric88 unique values
0 missing
Eig13_EA.ri.numeric89 unique values
0 missing
SM02_EA.ri.numeric118 unique values
0 missing
GGI3numeric56 unique values
0 missing
Eig13_EA.bo.numeric96 unique values
0 missing
SpMin2_Bh.p.numeric89 unique values
0 missing
D.Dtr06numeric109 unique values
0 missing
SM04_EA.ed.numeric114 unique values
0 missing
X5numeric120 unique values
0 missing
SpMax3_Bh.p.numeric101 unique values
0 missing
SpMax3_Bh.v.numeric105 unique values
0 missing
MWC10numeric119 unique values
0 missing
Eig04_EA.bo.numeric82 unique values
0 missing
SM14_AEA.ri.numeric82 unique values
0 missing
GGI6numeric84 unique values
0 missing
SM08_EA.ri.numeric136 unique values
0 missing
SM07_AEA.ed.numeric112 unique values
0 missing
MPC05numeric75 unique values
0 missing
ATS5mnumeric132 unique values
0 missing
ATS6pnumeric137 unique values
0 missing
SpMin4_Bh.e.numeric107 unique values
0 missing
SpMin4_Bh.i.numeric113 unique values
0 missing
GATS3inumeric112 unique values
0 missing
SM09_AEA.ed.numeric113 unique values
0 missing
SM06_EA.ri.numeric139 unique values
0 missing
MPC07numeric74 unique values
0 missing
SRW10numeric120 unique values
0 missing
MWC07numeric116 unique values
0 missing
SM06_EAnumeric111 unique values
0 missing
Eig13_AEA.bo.numeric77 unique values
0 missing
SM07_EAnumeric86 unique values
0 missing
SpMax4_Bh.p.numeric109 unique values
0 missing
MWC06numeric115 unique values
0 missing
ATS4vnumeric134 unique values
0 missing
SM05_EA.ri.numeric108 unique values
0 missing
SM07_AEA.bo.numeric121 unique values
0 missing
SM08_AEA.ed.numeric111 unique values
0 missing
Eta_betaPnumeric34 unique values
0 missing
RDSQnumeric121 unique values
0 missing
ATS7vnumeric137 unique values
0 missing
SM03_EA.ed.numeric64 unique values
0 missing
GGI9numeric83 unique values
0 missing
ATS7pnumeric136 unique values
0 missing
MPC04numeric61 unique values
0 missing
MWC08numeric117 unique values
0 missing
MWC09numeric116 unique values
0 missing
TWCnumeric117 unique values
0 missing
Eig02_AEA.dm.numeric54 unique values
0 missing
SpMin1_Bh.e.numeric67 unique values
0 missing
SpMin1_Bh.i.numeric66 unique values
0 missing

107 properties

147
Number of instances (rows) of the dataset.
65
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.
64
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
263.43
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.45
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
0.44
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.
0.45
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.
-1.68
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
1.73
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
2.17
Third quartile of kurtosis among attributes of the numeric type.
-0.28
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
172.41
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.46
Percentage of numeric attributes.
9.75
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.
1.54
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
1.29
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.
0.21
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
14.01
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.22
First quartile of kurtosis among attributes of the numeric type.
0.68
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
1.95
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.
-0.98
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
-0.37
Mean skewness among attributes of the numeric type.
0.3
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.
5.23
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
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.
-1.33
Minimum kurtosis among attributes of the numeric type.
4.04
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
5.76
Maximum kurtosis among attributes of the numeric type.
0.09
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
Define a new task