Data
QSAR-TID-12407

QSAR-TID-12407

active Sparse_ARFF Publicly available Visibility: public Uploaded 06-10-2015 by Noureddin Sadawi
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  • Machine Learning Mathematics MTLQSAR study_3
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Author: Dr Ivan Olier, Dr Jeremy Besnard, Dr Noureddin Sadawi, Dr Larisa Soldatova, Dr Crina Grosan, Prof Ross King, Dr Richard Bickerton, Prof Andrew Hopkins and Dr Willem van Hoorn Source: MetaQSAR project - September 2015 Please cite: This dataset contains QSAR data (from ChEMBL version 17) showing activity values (unit is pseudo-pCI50) of several compounds on drug target TID: 12407, and it has 66 rows and 1026 features (including IDs and class feature: MOLECULE_CHEMBL_ID and MEDIAN_PXC50). The features represent FCFP 1024bit Molecular Fingerprints which were generated from SMILES strings. They were obtained using the Pipeline Pilot program, Dassault Systèmes BIOVIA. Generating Fingerprints does not usually require missing value imputation as all bits are generated.

1026 features

MEDIAN_PXC50 (target)numeric58 unique values
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MOLECULE_CHEMBL_ID (row identifier)nominal66 unique values
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FCFP6_1024_744numeric1 unique values
0 missing
FCFP6_1024_1000numeric2 unique values
0 missing
FCFP6_1024_233numeric1 unique values
0 missing
FCFP6_1024_489numeric2 unique values
0 missing
FCFP6_1024_745numeric1 unique values
0 missing
FCFP6_1024_1001numeric2 unique values
0 missing
FCFP6_1024_234numeric2 unique values
0 missing
FCFP6_1024_490numeric1 unique values
0 missing
FCFP6_1024_746numeric1 unique values
0 missing
FCFP6_1024_1002numeric1 unique values
0 missing
FCFP6_1024_235numeric1 unique values
0 missing
FCFP6_1024_491numeric2 unique values
0 missing
FCFP6_1024_747numeric1 unique values
0 missing
FCFP6_1024_1003numeric1 unique values
0 missing
FCFP6_1024_236numeric2 unique values
0 missing
FCFP6_1024_492numeric1 unique values
0 missing
FCFP6_1024_748numeric2 unique values
0 missing
FCFP6_1024_1004numeric2 unique values
0 missing
FCFP6_1024_237numeric1 unique values
0 missing
FCFP6_1024_493numeric2 unique values
0 missing
FCFP6_1024_749numeric1 unique values
0 missing
FCFP6_1024_1005numeric1 unique values
0 missing
FCFP6_1024_238numeric1 unique values
0 missing
FCFP6_1024_494numeric1 unique values
0 missing
FCFP6_1024_750numeric1 unique values
0 missing
FCFP6_1024_1006numeric1 unique values
0 missing
FCFP6_1024_239numeric2 unique values
0 missing
FCFP6_1024_495numeric1 unique values
0 missing
FCFP6_1024_751numeric2 unique values
0 missing
FCFP6_1024_1007numeric1 unique values
0 missing
FCFP6_1024_240numeric2 unique values
0 missing
FCFP6_1024_496numeric2 unique values
0 missing
FCFP6_1024_752numeric1 unique values
0 missing
FCFP6_1024_1008numeric2 unique values
0 missing
FCFP6_1024_241numeric1 unique values
0 missing
FCFP6_1024_497numeric1 unique values
0 missing
FCFP6_1024_753numeric2 unique values
0 missing
FCFP6_1024_1009numeric2 unique values
0 missing
FCFP6_1024_242numeric1 unique values
0 missing
FCFP6_1024_498numeric1 unique values
0 missing
FCFP6_1024_754numeric1 unique values
0 missing
FCFP6_1024_1010numeric1 unique values
0 missing
FCFP6_1024_243numeric1 unique values
0 missing
FCFP6_1024_499numeric1 unique values
0 missing
FCFP6_1024_755numeric1 unique values
0 missing
FCFP6_1024_1011numeric1 unique values
0 missing
FCFP6_1024_244numeric1 unique values
0 missing
FCFP6_1024_500numeric1 unique values
0 missing
FCFP6_1024_756numeric1 unique values
0 missing
FCFP6_1024_1012numeric1 unique values
0 missing
FCFP6_1024_245numeric2 unique values
0 missing
FCFP6_1024_501numeric1 unique values
0 missing
FCFP6_1024_757numeric1 unique values
0 missing
FCFP6_1024_1013numeric1 unique values
0 missing
FCFP6_1024_246numeric1 unique values
0 missing
FCFP6_1024_502numeric1 unique values
0 missing
FCFP6_1024_758numeric1 unique values
0 missing
FCFP6_1024_1014numeric2 unique values
0 missing
FCFP6_1024_247numeric1 unique values
0 missing
FCFP6_1024_503numeric1 unique values
0 missing
FCFP6_1024_759numeric1 unique values
0 missing
FCFP6_1024_1015numeric1 unique values
0 missing
FCFP6_1024_248numeric1 unique values
0 missing
FCFP6_1024_504numeric2 unique values
0 missing

107 properties

66
Number of instances (rows) of the dataset.
1026
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.
1025
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
7.66
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.76
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
15.55
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
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.
-8.12
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
8.12
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
9.04
Third quartile of kurtosis among attributes of the numeric type.
0.49
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
0.96
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
99.9
Percentage of numeric attributes.
0.05
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.
0.1
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
12.66
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.
3.28
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
0.13
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.39
First quartile of kurtosis among attributes of the numeric type.
0.17
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
0
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.
-1.83
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
1.44
Mean skewness among attributes of the numeric type.
0
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.
0.09
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.
3.76
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.
-2.06
Minimum kurtosis among attributes of the numeric type.
0
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
66
Maximum kurtosis among attributes of the numeric type.
0
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

1 runs - estimation_procedure: Custom 10-fold Crossvalidation - target_feature: MEDIAN_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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