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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3151

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3151

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: CHEMBL3151 (TID: 12754), and it has 298 rows and 68 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.

70 features

pXC50 (target)numeric230 unique values
0 missing
molecule_id (row identifier)nominal298 unique values
0 missing
P_VSA_e_3numeric142 unique values
0 missing
nNnumeric13 unique values
0 missing
P_VSA_i_4numeric150 unique values
0 missing
P_VSA_MR_5numeric230 unique values
0 missing
CATS2D_01_DDnumeric3 unique values
0 missing
Minumeric56 unique values
0 missing
N.numeric100 unique values
0 missing
N.073numeric3 unique values
0 missing
P_VSA_s_5numeric41 unique values
0 missing
CATS2D_03_DAnumeric8 unique values
0 missing
nCconjnumeric9 unique values
0 missing
SaaNHnumeric39 unique values
0 missing
BIC0numeric139 unique values
0 missing
SIC0numeric151 unique values
0 missing
AACnumeric230 unique values
0 missing
AECCnumeric251 unique values
0 missing
ALOGPnumeric277 unique values
0 missing
ALOGP2numeric286 unique values
0 missing
AMRnumeric288 unique values
0 missing
AMWnumeric269 unique values
0 missing
ARRnumeric111 unique values
0 missing
ATS1enumeric247 unique values
0 missing
ATS1inumeric248 unique values
0 missing
ATS1mnumeric239 unique values
0 missing
ATS1pnumeric242 unique values
0 missing
ATS1snumeric241 unique values
0 missing
ATS1vnumeric237 unique values
0 missing
ATS2enumeric252 unique values
0 missing
ATS2inumeric250 unique values
0 missing
ATS2mnumeric241 unique values
0 missing
ATS2pnumeric249 unique values
0 missing
ATS2snumeric251 unique values
0 missing
ATS2vnumeric249 unique values
0 missing
ATS3enumeric260 unique values
0 missing
ATS3inumeric256 unique values
0 missing
ATS3mnumeric257 unique values
0 missing
ATS3pnumeric251 unique values
0 missing
ATS3snumeric255 unique values
0 missing
ATS3vnumeric259 unique values
0 missing
ATS4enumeric261 unique values
0 missing
ATS4inumeric258 unique values
0 missing
ATS4mnumeric262 unique values
0 missing
ATS4pnumeric269 unique values
0 missing
ATS4snumeric260 unique values
0 missing
ATS4vnumeric253 unique values
0 missing
ATS5enumeric270 unique values
0 missing
ATS5inumeric265 unique values
0 missing
ATS5mnumeric262 unique values
0 missing
ATS5pnumeric266 unique values
0 missing
ATS5snumeric264 unique values
0 missing
ATS5vnumeric264 unique values
0 missing
ATS6enumeric270 unique values
0 missing
ATS6inumeric265 unique values
0 missing
ATS6mnumeric262 unique values
0 missing
ATS6pnumeric271 unique values
0 missing
ATS6snumeric256 unique values
0 missing
ATS6vnumeric269 unique values
0 missing
ATS7enumeric275 unique values
0 missing
ATS7inumeric268 unique values
0 missing
ATS7mnumeric267 unique values
0 missing
ATS7pnumeric272 unique values
0 missing
ATS7snumeric266 unique values
0 missing
ATS7vnumeric264 unique values
0 missing
ATS8enumeric277 unique values
0 missing
ATS8inumeric271 unique values
0 missing
ATS8mnumeric267 unique values
0 missing
ATS8pnumeric270 unique values
0 missing
ATS8snumeric273 unique values
0 missing

107 properties

298
Number of instances (rows) of the dataset.
70
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.
69
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
101.93
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
0.23
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.
0.46
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.
-3.16
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
4.68
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
6.8
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
39
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.57
Percentage of numeric attributes.
4.96
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.
1.43
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
5.38
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.
1.08
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
7.77
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.41
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
3.49
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.
-0.04
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
0.55
Mean skewness among attributes of the numeric type.
0.33
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.
3.32
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.
3.81
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.
-0.74
Minimum kurtosis among attributes of the numeric type.
3.94
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
28.63
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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