Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1916

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1916

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: CHEMBL1916 (TID: 218), and it has 613 rows and 69 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.

71 features

pXC50 (target)numeric440 unique values
0 missing
molecule_id (row identifier)nominal613 unique values
0 missing
N.068numeric4 unique values
0 missing
nRNR2numeric4 unique values
0 missing
D.Dtr09numeric170 unique values
0 missing
SsssNnumeric296 unique values
0 missing
nR09numeric6 unique values
0 missing
piIDnumeric433 unique values
0 missing
SdsCHnumeric107 unique values
0 missing
NsssNnumeric5 unique values
0 missing
O.060numeric6 unique values
0 missing
C.016numeric6 unique values
0 missing
SssOnumeric302 unique values
0 missing
nArCNOnumeric2 unique values
0 missing
NNRSnumeric16 unique values
0 missing
NssOnumeric8 unique values
0 missing
NdsCHnumeric6 unique values
0 missing
CATS2D_04_AAnumeric7 unique values
0 missing
IDDEnumeric275 unique values
0 missing
D.Dtr10numeric215 unique values
0 missing
SaasCnumeric519 unique values
0 missing
IC5numeric369 unique values
0 missing
C.039numeric3 unique values
0 missing
Eig08_AEA.bo.numeric361 unique values
0 missing
Eig09_AEA.bo.numeric350 unique values
0 missing
SM02_EA.ed.numeric311 unique values
0 missing
SM02_AEA.bo.numeric266 unique values
0 missing
Wapnumeric412 unique values
0 missing
TPCnumeric357 unique values
0 missing
H.047numeric34 unique values
0 missing
Eig10_AEA.dm.numeric411 unique values
0 missing
SpAD_EA.bo.numeric477 unique values
0 missing
piPC01numeric93 unique values
0 missing
SCBOnumeric93 unique values
0 missing
Chi1_AEA.bo.numeric409 unique values
0 missing
Chi1_AEA.dm.numeric409 unique values
0 missing
Chi1_AEA.ed.numeric409 unique values
0 missing
Chi1_AEA.ri.numeric409 unique values
0 missing
Chi1_EAnumeric409 unique values
0 missing
Eig10_EA.ri.numeric395 unique values
0 missing
SpMax4_Bh.p.numeric354 unique values
0 missing
TIC1numeric481 unique values
0 missing
Eig09_EA.bo.numeric369 unique values
0 missing
Eig07_AEA.bo.numeric351 unique values
0 missing
nR10numeric6 unique values
0 missing
SpMin3_Bh.p.numeric268 unique values
0 missing
CATS2D_03_AAnumeric7 unique values
0 missing
nCICnumeric9 unique values
0 missing
TRSnumeric32 unique values
0 missing
MPC01numeric47 unique values
0 missing
MWC01numeric47 unique values
0 missing
nBOnumeric47 unique values
0 missing
SRW02numeric47 unique values
0 missing
SpAD_AEA.bo.numeric473 unique values
0 missing
Eig15_AEA.bo.numeric315 unique values
0 missing
IC4numeric371 unique values
0 missing
Chi1_EA.ri.numeric531 unique values
0 missing
RDSQnumeric446 unique values
0 missing
SpMax4_Bh.v.numeric349 unique values
0 missing
SRW07numeric34 unique values
0 missing
SMTInumeric439 unique values
0 missing
Eta_betaSnumeric100 unique values
0 missing
AMRnumeric511 unique values
0 missing
UNIPnumeric197 unique values
0 missing
GMTInumeric439 unique values
0 missing
IDDMnumeric263 unique values
0 missing
BIC0numeric142 unique values
0 missing
SIC0numeric152 unique values
0 missing
nArORnumeric6 unique values
0 missing
X2solnumeric440 unique values
0 missing
HVcpxnumeric374 unique values
0 missing

107 properties

613
Number of instances (rows) of the dataset.
71
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.
70
Number of numeric attributes.
1
Number of nominal attributes.
5.33
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
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
1052.55
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.7
First quartile of kurtosis 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
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
1.02
First quartile of means among attributes of the numeric type.
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
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.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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.
-0.09
First quartile of skewness among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.87
Mean skewness among attributes of the numeric type.
0.48
First quartile of 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
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.
2728.63
Mean standard deviation of attributes of the numeric type.
0.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient 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.
3.76
Second quartile (Median) of means among attributes of the numeric type.
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.
-1.32
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
395.92
Maximum kurtosis among attributes of the numeric type.
0.13
Minimum of means among attributes of the numeric type.
0.58
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
49857.92
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.2
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.12
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.
Third quartile of entropy among attributes.
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.
-1.13
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.71
Third quartile of kurtosis among attributes of the numeric type.
-0.07
Average class difference between consecutive instances.
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
18.29
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
14.67
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
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
167938.11
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.59
Percentage of numeric 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
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.
1.41
Percentage of nominal attributes.
1.29
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
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
6.91
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.

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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