Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4306

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4306

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: CHEMBL4306 (TID: 10198), and it has 480 rows and 66 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.

68 features

pXC50 (target)numeric280 unique values
0 missing
molecule_id (row identifier)nominal480 unique values
0 missing
MPC08numeric171 unique values
0 missing
MPC09numeric185 unique values
0 missing
MPC10numeric192 unique values
0 missing
MPC07numeric153 unique values
0 missing
SpMax7_Bh.m.numeric242 unique values
0 missing
Eta_sh_ynumeric209 unique values
0 missing
nArNHRnumeric2 unique values
0 missing
Eig04_EA.ed.numeric272 unique values
0 missing
SM13_AEA.dm.numeric272 unique values
0 missing
Eig08_AEA.dm.numeric261 unique values
0 missing
Eig07_AEA.dm.numeric264 unique values
0 missing
C.035numeric3 unique values
0 missing
SdssCnumeric339 unique values
0 missing
CATS2D_01_AAnumeric5 unique values
0 missing
GATS1mnumeric242 unique values
0 missing
Chi1_EA.dm.numeric351 unique values
0 missing
C.019numeric2 unique values
0 missing
Eig04_AEA.ed.numeric238 unique values
0 missing
SsClnumeric125 unique values
0 missing
P_VSA_MR_7numeric65 unique values
0 missing
nPyrazolesnumeric2 unique values
0 missing
GATS2mnumeric296 unique values
0 missing
C.017numeric3 unique values
0 missing
nR.Ctnumeric3 unique values
0 missing
Chi0_EA.dm.numeric332 unique values
0 missing
JGI6numeric25 unique values
0 missing
MPC05numeric101 unique values
0 missing
PW3numeric69 unique values
0 missing
nR.Csnumeric4 unique values
0 missing
piPC10numeric311 unique values
0 missing
nCICnumeric5 unique values
0 missing
TRSnumeric17 unique values
0 missing
Cl.089numeric4 unique values
0 missing
nCLnumeric4 unique values
0 missing
NsClnumeric4 unique values
0 missing
P_VSA_e_4numeric4 unique values
0 missing
P_VSA_m_4numeric22 unique values
0 missing
P_VSA_LogP_8numeric12 unique values
0 missing
GATS3mnumeric276 unique values
0 missing
Eig06_AEA.dm.numeric271 unique values
0 missing
MPC06numeric128 unique values
0 missing
Eta_betaS_Anumeric90 unique values
0 missing
SaasNnumeric124 unique values
0 missing
MPC04numeric74 unique values
0 missing
nCIRnumeric7 unique values
0 missing
piPC08numeric320 unique values
0 missing
RBFnumeric105 unique values
0 missing
LLS_02numeric5 unique values
0 missing
piPC09numeric311 unique values
0 missing
X5Anumeric26 unique values
0 missing
NaasNnumeric3 unique values
0 missing
IC2numeric346 unique values
0 missing
X2Anumeric48 unique values
0 missing
piPC06numeric319 unique values
0 missing
Eig05_EA.ed.numeric299 unique values
0 missing
SM14_AEA.dm.numeric299 unique values
0 missing
GATS1pnumeric259 unique values
0 missing
TPCnumeric256 unique values
0 missing
nArXnumeric5 unique values
0 missing
Eig04_AEA.dm.numeric255 unique values
0 missing
piPC07numeric308 unique values
0 missing
CATS2D_03_DLnumeric12 unique values
0 missing
nCrsnumeric8 unique values
0 missing
Wapnumeric315 unique values
0 missing
Eig08_AEA.ed.numeric252 unique values
0 missing
N.073numeric3 unique values
0 missing

62 properties

480
Number of instances (rows) of the dataset.
68
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.
67
Number of numeric attributes.
1
Number of nominal 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.
Second quartile (Median) of entropy among attributes.
0.14
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.22
Second quartile (Median) of kurtosis 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.
0.2
Mean skewness among attributes of the numeric type.
3.06
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
330.1
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.06
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.1
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
5.58
Maximum kurtosis among attributes of the numeric type.
0.02
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
26932.51
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.39
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
98.53
Percentage of numeric attributes.
6.45
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-1.24
Minimum skewness among attributes of the numeric type.
1.47
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
1.72
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.03
Third quartile of skewness among attributes of the numeric type.
21896.93
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.64
First quartile of kurtosis among attributes of the numeric type.
1.26
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
0.47
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
0.03
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
407.37
Mean of means among attributes of the numeric type.
-0.41
First quartile of skewness among attributes of the numeric type.
0.44
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.31
First quartile of standard deviation of 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
Define a new task