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QSAR-TID-8

QSAR-TID-8

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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: 8, and it has 1739 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)numeric514 unique values
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MOLECULE_CHEMBL_ID (row identifier)nominal1739 unique values
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FCFP6_1024_0numeric2 unique values
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FCFP6_1024_936numeric2 unique values
0 missing
FCFP6_1024_937numeric2 unique values
0 missing
FCFP6_1024_938numeric2 unique values
0 missing
FCFP6_1024_939numeric2 unique values
0 missing
FCFP6_1024_940numeric2 unique values
0 missing
FCFP6_1024_941numeric2 unique values
0 missing
FCFP6_1024_942numeric2 unique values
0 missing
FCFP6_1024_943numeric2 unique values
0 missing
FCFP6_1024_944numeric2 unique values
0 missing
FCFP6_1024_945numeric2 unique values
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FCFP6_1024_946numeric2 unique values
0 missing
FCFP6_1024_947numeric2 unique values
0 missing
FCFP6_1024_948numeric2 unique values
0 missing
FCFP6_1024_949numeric2 unique values
0 missing
FCFP6_1024_950numeric2 unique values
0 missing
FCFP6_1024_951numeric2 unique values
0 missing
FCFP6_1024_952numeric2 unique values
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FCFP6_1024_953numeric2 unique values
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FCFP6_1024_954numeric2 unique values
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FCFP6_1024_955numeric2 unique values
0 missing
FCFP6_1024_956numeric2 unique values
0 missing
FCFP6_1024_957numeric2 unique values
0 missing
FCFP6_1024_958numeric2 unique values
0 missing
FCFP6_1024_959numeric2 unique values
0 missing
FCFP6_1024_960numeric2 unique values
0 missing
FCFP6_1024_961numeric2 unique values
0 missing
FCFP6_1024_962numeric2 unique values
0 missing
FCFP6_1024_963numeric2 unique values
0 missing
FCFP6_1024_964numeric2 unique values
0 missing
FCFP6_1024_965numeric2 unique values
0 missing
FCFP6_1024_966numeric2 unique values
0 missing
FCFP6_1024_967numeric2 unique values
0 missing
FCFP6_1024_968numeric2 unique values
0 missing
FCFP6_1024_969numeric2 unique values
0 missing
FCFP6_1024_970numeric2 unique values
0 missing
FCFP6_1024_971numeric2 unique values
0 missing
FCFP6_1024_972numeric2 unique values
0 missing
FCFP6_1024_973numeric2 unique values
0 missing
FCFP6_1024_974numeric2 unique values
0 missing
FCFP6_1024_975numeric2 unique values
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FCFP6_1024_976numeric2 unique values
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FCFP6_1024_977numeric2 unique values
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FCFP6_1024_978numeric2 unique values
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FCFP6_1024_979numeric2 unique values
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FCFP6_1024_980numeric2 unique values
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FCFP6_1024_981numeric2 unique values
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FCFP6_1024_982numeric2 unique values
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FCFP6_1024_983numeric2 unique values
0 missing
FCFP6_1024_984numeric2 unique values
0 missing
FCFP6_1024_985numeric2 unique values
0 missing
FCFP6_1024_986numeric2 unique values
0 missing
FCFP6_1024_987numeric2 unique values
0 missing
FCFP6_1024_988numeric2 unique values
0 missing
FCFP6_1024_989numeric2 unique values
0 missing
FCFP6_1024_990numeric2 unique values
0 missing
FCFP6_1024_991numeric2 unique values
0 missing
FCFP6_1024_992numeric2 unique values
0 missing
FCFP6_1024_993numeric2 unique values
0 missing
FCFP6_1024_994numeric2 unique values
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FCFP6_1024_995numeric2 unique values
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FCFP6_1024_996numeric2 unique values
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FCFP6_1024_997numeric2 unique values
0 missing
Show first 100 features

107 properties

NumberOfInstances
1739
Number of instances (rows) of the dataset.
NumberOfFeatures
1026
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
1025
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.19
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.59
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1739
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
6.19
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
41.7
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1.14
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
44.52
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.07
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
5.83
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.19
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-2
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-29.46
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.02
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.9
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.1
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
12.67
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.02
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
3.8
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.13
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
29.22
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.03
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
5.58
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.17
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
55.14
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.06
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
7.56
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.23
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

12 tasks

Supervised Regression on QSAR-TID-8
1 runs - estimation_procedure: Custom 10-fold Crossvalidation - target_feature: MEDIAN_PXC50
Clustering on QSAR-TID-8
0 runs
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
Clustering on QSAR-TID-8
0 runs - estimation_procedure: 50 times Clustering
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