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qsar-biodeg

qsar-biodeg

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Author: Source: UCI Please cite: Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V. (2013). Quantitative Structure - Activity Relationship models for ready biodegradability of chemicals. Journal of Chemical Information and Modeling, 53, 867-878 QSAR biodegradation Data Set * Abstract: Data set containing values for 41 attributes (molecular descriptors) used to classify 1055 chemicals into 2 classes (ready and not ready biodegradable). * Source: Kamel Mansouri, Tine Ringsted, Davide Ballabio (davide.ballabio '@' unimib.it), Roberto Todeschini, Viviana Consonni, Milano Chemometrics and QSAR Research Group (http://michem.disat.unimib.it/chm/), Università degli Studi Milano – Bicocca, Milano (Italy) * Data Set Information: The QSAR biodegradation dataset was built in the Milano Chemometrics and QSAR Research Group (Università degli Studi Milano – Bicocca, Milano, Italy). The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement n. 238701 of Marie Curie ITN Environmental Chemoinformatics (ECO) project. The data have been used to develop QSAR (Quantitative Structure Activity Relationships) models for the study of the relationships between chemical structure and biodegradation of molecules. Biodegradation experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE). Classification models were developed in order to discriminate ready (356) and not ready (699) biodegradable molecules by means of three different modelling methods: k Nearest Neighbours, Partial Least Squares Discriminant Analysis and Support Vector Machines. Details on attributes (molecular descriptors) selected in each model can be found in the quoted reference: Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V. (2013). Quantitative Structure - Activity Relationship models for ready biodegradability of chemicals. Journal of Chemical Information and Modeling, 53, 867-878. * Attribute Information: 41 molecular descriptors and 1 experimental class: 1) SpMax_L: Leading eigenvalue from Laplace matrix 2) J_Dz(e): Balaban-like index from Barysz matrix weighted by Sanderson electronegativity 3) nHM: Number of heavy atoms 4) F01[N-N]: Frequency of N-N at topological distance 1 5) F04[C-N]: Frequency of C-N at topological distance 4 6) NssssC: Number of atoms of type ssssC 7) nCb-: Number of substituted benzene C(sp2) 8) C%: Percentage of C atoms 9) nCp: Number of terminal primary C(sp3) 10) nO: Number of oxygen atoms 11) F03[C-N]: Frequency of C-N at topological distance 3 12) SdssC: Sum of dssC E-states 13) HyWi_B(m): Hyper-Wiener-like index (log function) from Burden matrix weighted by mass 14) LOC: Lopping centric index 15) SM6_L: Spectral moment of order 6 from Laplace matrix 16) F03[C-O]: Frequency of C - O at topological distance 3 17) Me: Mean atomic Sanderson electronegativity (scaled on Carbon atom) 18) Mi: Mean first ionization potential (scaled on Carbon atom) 19) nN-N: Number of N hydrazines 20) nArNO2: Number of nitro groups (aromatic) 21) nCRX3: Number of CRX3 22) SpPosA_B(p): Normalized spectral positive sum from Burden matrix weighted by polarizability 23) nCIR: Number of circuits 24) B01[C-Br]: Presence/absence of C - Br at topological distance 1 25) B03[C-Cl]: Presence/absence of C - Cl at topological distance 3 26) N-073: Ar2NH / Ar3N / Ar2N-Al / R..N..R 27) SpMax_A: Leading eigenvalue from adjacency matrix (Lovasz-Pelikan index) 28) Psi_i_1d: Intrinsic state pseudoconnectivity index - type 1d 29) B04[C-Br]: Presence/absence of C - Br at topological distance 4 30) SdO: Sum of dO E-states 31) TI2_L: Second Mohar index from Laplace matrix 32) nCrt: Number of ring tertiary C(sp3) 33) C-026: R--CX--R 34) F02[C-N]: Frequency of C - N at topological distance 2 35) nHDon: Number of donor atoms for H-bonds (N and O) 36) SpMax_B(m): Leading eigenvalue from Burden matrix weighted by mass 37) Psi_i_A: Intrinsic state pseudoconnectivity index - type S average 38) nN: Number of Nitrogen atoms 39) SM6_B(m): Spectral moment of order 6 from Burden matrix weighted by mass 40) nArCOOR: Number of esters (aromatic) 41) nX: Number of halogen atoms 42) experimental class: ready biodegradable (RB) and not ready biodegradable (NRB) * Relevant Papers: Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V. (2013). Quantitative Structure - Activity Relationship models for ready biodegradability of chemicals. Journal of Chemical Information and Modeling, 53, 867-878

42 features

Class (target)nominal2 unique values
0 missing
V23numeric13 unique values
0 missing
V22numeric352 unique values
0 missing
V24numeric2 unique values
0 missing
V25numeric2 unique values
0 missing
V26numeric4 unique values
0 missing
V27numeric329 unique values
0 missing
V28numeric205 unique values
0 missing
V29numeric2 unique values
0 missing
V30numeric470 unique values
0 missing
V31numeric553 unique values
0 missing
V32numeric8 unique values
0 missing
V33numeric11 unique values
0 missing
V34numeric16 unique values
0 missing
V35numeric8 unique values
0 missing
V36numeric705 unique values
0 missing
V37numeric624 unique values
0 missing
V38numeric8 unique values
0 missing
V39numeric862 unique values
0 missing
V40numeric5 unique values
0 missing
V41numeric17 unique values
0 missing
V12numeric384 unique values
0 missing
V2numeric1022 unique values
0 missing
V3numeric11 unique values
0 missing
V4numeric4 unique values
0 missing
V5numeric16 unique values
0 missing
V6numeric13 unique values
0 missing
V7numeric15 unique values
0 missing
V8numeric188 unique values
0 missing
V9numeric15 unique values
0 missing
V10numeric12 unique values
0 missing
V11numeric21 unique values
0 missing
V1numeric440 unique values
0 missing
V13numeric756 unique values
0 missing
V14numeric373 unique values
0 missing
V15numeric510 unique values
0 missing
V16numeric24 unique values
0 missing
V17numeric167 unique values
0 missing
V18numeric125 unique values
0 missing
V19numeric3 unique values
0 missing
V20numeric4 unique values
0 missing
V21numeric4 unique values
0 missing
Show all 42 features

107 properties

NumberOfInstances
1055
Number of instances (rows) of the dataset.
NumberOfFeatures
42
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
41
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.82
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
0.18
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
0.58
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
0.82
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
0.18
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
0.58
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
0.82
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
0.18
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
0.58
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
0.92
Entropy of the target attribute values.
DecisionStumpAUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.04
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
66.26
Percentage of instances belonging to the most frequent class.
MajorityClassSize
699
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
812.72
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
37.06
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
26.85
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
11.9
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
44.71
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
2.68
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
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.8
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1.59
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.22
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.2
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.76
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.03
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
33.74
Percentage of instances belonging to the least frequent class.
MinorityClassSize
356
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
2.38
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
97.62
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2.38
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
3.38
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.1
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
1.41
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.24
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
11.77
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1.13
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
2.62
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.83
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
46.89
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
2.61
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
5.71
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
2.03
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: Leave one out - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
Supervised Classification on qsar-biodeg
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Learning Curve on qsar-biodeg
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
Learning Curve on qsar-biodeg
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
Supervised Data Stream Classification on qsar-biodeg
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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