Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4361

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4361

deactivated ARFF Publicly available Visibility: public Uploaded 15-07-2016 by Noureddin Sadawi
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
This dataset contains QSAR data (from ChEMBL version 17) showing activity values (unit is pseudo-pCI50) of several compounds on drug target ChEMBL_ID: CHEMBL4361 (TID: 100468), and it has 1457 rows and 71 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.

73 features

pXC50 (target)numeric870 unique values
0 missing
molecule_id (row identifier)nominal1457 unique values
0 missing
SpMax1_Bh.p.numeric344 unique values
0 missing
SpMax1_Bh.v.numeric327 unique values
0 missing
MPC07numeric288 unique values
0 missing
SpMin2_Bh.s.numeric471 unique values
0 missing
D.Dtr12numeric71 unique values
0 missing
SM13_EA.ri.numeric1123 unique values
0 missing
SM14_EA.ri.numeric1124 unique values
0 missing
MWC10numeric924 unique values
0 missing
SpMin2_Bh.m.numeric387 unique values
0 missing
nR12numeric4 unique values
0 missing
TWCnumeric896 unique values
0 missing
ALOGP2numeric1346 unique values
0 missing
C.025numeric15 unique values
0 missing
SM12_EA.ri.numeric1105 unique values
0 missing
ALOGPnumeric1211 unique values
0 missing
piPC06numeric981 unique values
0 missing
Eig01_EA.ri.numeric595 unique values
0 missing
SpDiam_EA.ri.numeric604 unique values
0 missing
SpMax_EA.ri.numeric595 unique values
0 missing
SpMax1_Bh.i.numeric298 unique values
0 missing
SM11_EA.ri.numeric1104 unique values
0 missing
D.Dtr11numeric52 unique values
0 missing
SM10_EA.ri.numeric1081 unique values
0 missing
CATS2D_04_LLnumeric35 unique values
0 missing
TPCnumeric1026 unique values
0 missing
SM09_EA.ri.numeric1038 unique values
0 missing
nCIRnumeric20 unique values
0 missing
SM08_EA.ri.numeric984 unique values
0 missing
piPC05numeric933 unique values
0 missing
D.Dtr10numeric450 unique values
0 missing
MPC10numeric381 unique values
0 missing
MPC05numeric202 unique values
0 missing
CATS2D_03_LLnumeric39 unique values
0 missing
Rbridnumeric8 unique values
0 missing
piPC10numeric1127 unique values
0 missing
piPC04numeric849 unique values
0 missing
MWC07numeric851 unique values
0 missing
MPC09numeric362 unique values
0 missing
MWC08numeric859 unique values
0 missing
MPC08numeric321 unique values
0 missing
nR11numeric4 unique values
0 missing
piPC09numeric1118 unique values
0 missing
CATS2D_02_LLnumeric41 unique values
0 missing
Eig05_EA.ed.numeric1147 unique values
0 missing
SM14_AEA.dm.numeric1147 unique values
0 missing
SRW10numeric859 unique values
0 missing
SM05_EA.ri.numeric913 unique values
0 missing
piPC07numeric1042 unique values
0 missing
SM06_EA.ri.numeric900 unique values
0 missing
SM07_EA.ri.numeric984 unique values
0 missing
MWC06numeric823 unique values
0 missing
MPC06numeric240 unique values
0 missing
piIDnumeric1244 unique values
0 missing
Eig05_AEA.ed.numeric908 unique values
0 missing
SpMax1_Bh.e.numeric303 unique values
0 missing
Eig01_EA.bo.numeric690 unique values
0 missing
SM11_AEA.ri.numeric690 unique values
0 missing
SpDiam_EA.bo.numeric702 unique values
0 missing
SpMax_EA.bo.numeric690 unique values
0 missing
Eig01_AEA.ri.numeric594 unique values
0 missing
SpMax_AEA.ri.numeric594 unique values
0 missing
H.046numeric27 unique values
0 missing
SM11_EA.bo.numeric1078 unique values
0 missing
SM10_EA.bo.numeric1046 unique values
0 missing
piPC08numeric1088 unique values
0 missing
SpMin1_Bh.i.numeric309 unique values
0 missing
SM15_EA.ri.numeric1149 unique values
0 missing
TRSnumeric38 unique values
0 missing
SM12_EA.bo.numeric1106 unique values
0 missing
CATS2D_01_LLnumeric33 unique values
0 missing
SM09_EA.bo.numeric1057 unique values
0 missing

62 properties

1457
Number of instances (rows) of the dataset.
73
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.
72
Number of numeric attributes.
1
Number of nominal attributes.
3.75
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
6.25
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.29
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
3.61
Mean standard deviation of attributes of the numeric type.
-0.47
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.62
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.19
Minimum kurtosis among attributes of the numeric type.
0.04
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
452.97
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
9.12
Third quartile of kurtosis among attributes of the numeric type.
52.36
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.63
Percentage of numeric attributes.
10.59
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-2.4
Minimum skewness among attributes of the numeric type.
1.37
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
0.07
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.38
Third quartile of skewness among attributes of the numeric type.
18.94
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
2.25
First quartile of kurtosis among attributes of the numeric type.
1.11
Third quartile of standard deviation of attributes of the numeric type.
96.37
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
4.36
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
14.15
Mean kurtosis among attributes of the numeric type.
-1.02
First quartile of skewness among attributes of the numeric type.
8.16
Mean of means among attributes of the numeric type.
0.39
First quartile of standard deviation of attributes of the numeric type.
0.46
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

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
Define a new task