Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2007629

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2007629

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: CHEMBL2007629 (TID: 104487), and it has 396 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)numeric291 unique values
0 missing
molecule_id (row identifier)nominal396 unique values
0 missing
AACnumeric295 unique values
0 missing
AECCnumeric341 unique values
0 missing
ALOGPnumeric369 unique values
0 missing
ALOGP2numeric384 unique values
0 missing
AMRnumeric389 unique values
0 missing
AMWnumeric362 unique values
0 missing
ARRnumeric144 unique values
0 missing
ATS1enumeric312 unique values
0 missing
ATS1inumeric311 unique values
0 missing
ATS1mnumeric299 unique values
0 missing
ATS1pnumeric303 unique values
0 missing
ATS1snumeric299 unique values
0 missing
ATS1vnumeric300 unique values
0 missing
ATS2enumeric321 unique values
0 missing
ATS2inumeric316 unique values
0 missing
ATS2mnumeric304 unique values
0 missing
ATS2pnumeric305 unique values
0 missing
ATS2snumeric321 unique values
0 missing
ATS2vnumeric290 unique values
0 missing
ATS3enumeric328 unique values
0 missing
ATS3inumeric331 unique values
0 missing
ATS3mnumeric311 unique values
0 missing
ATS3pnumeric311 unique values
0 missing
ATS3snumeric317 unique values
0 missing
ATS3vnumeric312 unique values
0 missing
ATS4enumeric330 unique values
0 missing
ATS4inumeric339 unique values
0 missing
ATS4mnumeric306 unique values
0 missing
ATS4pnumeric320 unique values
0 missing
ATS4snumeric334 unique values
0 missing
ATS4vnumeric327 unique values
0 missing
ATS5enumeric340 unique values
0 missing
ATS5inumeric344 unique values
0 missing
ATS5mnumeric332 unique values
0 missing
ATS5pnumeric335 unique values
0 missing
ATS5snumeric343 unique values
0 missing
ATS5vnumeric338 unique values
0 missing
ATS6enumeric345 unique values
0 missing
ATS6inumeric335 unique values
0 missing
ATS6mnumeric331 unique values
0 missing
ATS6pnumeric341 unique values
0 missing
ATS6snumeric345 unique values
0 missing
ATS6vnumeric340 unique values
0 missing
ATS7enumeric346 unique values
0 missing
ATS7inumeric353 unique values
0 missing
ATS7mnumeric343 unique values
0 missing
ATS7pnumeric350 unique values
0 missing
ATS7snumeric343 unique values
0 missing
ATS7vnumeric331 unique values
0 missing
ATS8enumeric352 unique values
0 missing
ATS8inumeric349 unique values
0 missing
ATS8mnumeric347 unique values
0 missing
ATS8pnumeric338 unique values
0 missing
ATS8snumeric344 unique values
0 missing
ATS8vnumeric344 unique values
0 missing
ATSC1enumeric195 unique values
0 missing
ATSC1inumeric290 unique values
0 missing
ATSC1mnumeric378 unique values
0 missing
ATSC1pnumeric370 unique values
0 missing
ATSC1snumeric384 unique values
0 missing
ATSC1vnumeric378 unique values
0 missing
ATSC2enumeric280 unique values
0 missing
ATSC2inumeric331 unique values
0 missing
ATSC2mnumeric387 unique values
0 missing
ATSC2pnumeric380 unique values
0 missing
ATSC2snumeric389 unique values
0 missing
ATSC2vnumeric383 unique values
0 missing
ATSC3enumeric290 unique values
0 missing
ATSC3inumeric329 unique values
0 missing

107 properties

396
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.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
106.51
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.03
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
0.18
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.
0.37
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.
-1.85
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
4.56
Maximum skewness among attributes of the numeric type.
0.08
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
5.35
Third quartile of kurtosis among attributes of the numeric type.
0.62
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
27.72
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.59
Percentage of numeric attributes.
5.2
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.
1.41
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
8.56
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.
1.38
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
6.58
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.57
First quartile of kurtosis among attributes of the numeric type.
0.49
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
3.7
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.
-0.43
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
0.61
Mean skewness among attributes of the numeric type.
0.26
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.
1.67
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.
2.72
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.
0.12
Minimum kurtosis among attributes of the numeric type.
4.07
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
48.41
Maximum kurtosis among attributes of the numeric type.
0.11
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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