Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1892

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1892

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: CHEMBL1892 (TID: 184), and it has 224 rows and 67 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.

69 features

pXC50 (target)numeric173 unique values
0 missing
molecule_id (row identifier)nominal224 unique values
0 missing
ATSC3snumeric209 unique values
0 missing
CATS2D_04_DNnumeric8 unique values
0 missing
ATSC3enumeric185 unique values
0 missing
GATS7enumeric191 unique values
0 missing
MATS7mnumeric145 unique values
0 missing
ATSC2snumeric207 unique values
0 missing
ATSC1enumeric153 unique values
0 missing
ATSC5snumeric209 unique values
0 missing
GATS7mnumeric172 unique values
0 missing
nHAccnumeric26 unique values
0 missing
ATSC2enumeric168 unique values
0 missing
TPSA.NO.numeric91 unique values
0 missing
nHDonnumeric17 unique values
0 missing
CATS2D_07_DAnumeric13 unique values
0 missing
ATSC8enumeric195 unique values
0 missing
ATSC4snumeric209 unique values
0 missing
SpMax5_Bh.s.numeric90 unique values
0 missing
CATS2D_07_DDnumeric8 unique values
0 missing
nHetnumeric26 unique values
0 missing
MATS7snumeric175 unique values
0 missing
ATSC1snumeric195 unique values
0 missing
ATSC8snumeric207 unique values
0 missing
ATSC4enumeric196 unique values
0 missing
CATS2D_04_DLnumeric21 unique values
0 missing
SsOHnumeric197 unique values
0 missing
SsssCHnumeric164 unique values
0 missing
P_VSA_e_5numeric67 unique values
0 missing
GATS7snumeric180 unique values
0 missing
P_VSA_MR_2numeric85 unique values
0 missing
nHMnumeric8 unique values
0 missing
CATS2D_02_DLnumeric16 unique values
0 missing
CATS2D_05_DNnumeric11 unique values
0 missing
SpMax3_Bh.s.numeric42 unique values
0 missing
SpMax8_Bh.s.numeric151 unique values
0 missing
S3Knumeric187 unique values
0 missing
MATS7enumeric178 unique values
0 missing
ATSC6snumeric209 unique values
0 missing
SpMax4_Bh.s.numeric51 unique values
0 missing
TIEnumeric209 unique values
0 missing
Hynumeric152 unique values
0 missing
CATS2D_05_DDnumeric7 unique values
0 missing
Uindexnumeric178 unique values
0 missing
SdssCnumeric197 unique values
0 missing
P_VSA_p_4numeric11 unique values
0 missing
CATS2D_01_ANnumeric15 unique values
0 missing
CATS2D_01_DNnumeric13 unique values
0 missing
CATS2D_03_ALnumeric26 unique values
0 missing
P_VSA_LogP_4numeric74 unique values
0 missing
ATSC6enumeric196 unique values
0 missing
BLInumeric159 unique values
0 missing
X1Avnumeric109 unique values
0 missing
CATS2D_02_NLnumeric14 unique values
0 missing
NdssCnumeric16 unique values
0 missing
P_VSA_LogP_2numeric93 unique values
0 missing
DLS_consnumeric47 unique values
0 missing
SpMax7_Bh.s.numeric120 unique values
0 missing
SpMax1_Bh.s.numeric29 unique values
0 missing
P_VSA_m_3numeric71 unique values
0 missing
SpMax6_Bh.s.numeric92 unique values
0 missing
DLS_06numeric6 unique values
0 missing
CATS2D_06_DAnumeric14 unique values
0 missing
DLS_03numeric5 unique values
0 missing
SM06_EA.bo.numeric176 unique values
0 missing
SpMax1_Bh.m.numeric130 unique values
0 missing
P_VSA_s_6numeric86 unique values
0 missing
P_VSA_v_2numeric98 unique values
0 missing
SAaccnumeric93 unique values
0 missing

107 properties

224
Number of instances (rows) of the dataset.
69
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.
68
Number of numeric attributes.
1
Number of nominal attributes.
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.
-1.05
Minimum kurtosis among attributes of the numeric type.
4.89
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
19.44
Maximum kurtosis among attributes of the numeric type.
-3.46
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
248.67
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.51
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
0.31
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.
2.34
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.
-2.85
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
4.17
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
13.52
Third quartile of kurtosis among attributes of the numeric type.
-0.15
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
242.26
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
98.55
Percentage of numeric attributes.
43.78
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.
1.45
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
8.18
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.
3.3
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.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
49.5
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.87
First quartile of kurtosis among attributes of the numeric type.
39.14
Third quartile of standard deviation of attributes of the numeric type.
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
1.04
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
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.
Error rate 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.
0.53
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
1.88
Mean skewness among attributes of the numeric type.
0.85
First quartile of standard deviation of attributes of the numeric type.
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.
40.13
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
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.
8.95
Second quartile (Median) of kurtosis among attributes of the numeric type.

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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