Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4564

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4564

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: CHEMBL4564 (TID: 12409), and it has 119 rows and 63 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.

65 features

pXC50 (target)numeric97 unique values
0 missing
molecule_id (row identifier)nominal119 unique values
0 missing
Minumeric24 unique values
0 missing
Hynumeric79 unique values
0 missing
P_VSA_e_3numeric30 unique values
0 missing
P_VSA_i_4numeric29 unique values
0 missing
TPSA.NO.numeric45 unique values
0 missing
SpMin7_Bh.v.numeric79 unique values
0 missing
AACnumeric78 unique values
0 missing
ATSC1enumeric80 unique values
0 missing
ATSC3enumeric101 unique values
0 missing
C.numeric47 unique values
0 missing
C.043numeric2 unique values
0 missing
Eig02_EA.dm.numeric16 unique values
0 missing
Eig03_EA.dm.numeric19 unique values
0 missing
Eig04_EA.dm.numeric15 unique values
0 missing
IC0numeric78 unique values
0 missing
MATS4snumeric90 unique values
0 missing
NaaOnumeric2 unique values
0 missing
nFuranesnumeric2 unique values
0 missing
P_VSA_MR_2numeric34 unique values
0 missing
SaaOnumeric13 unique values
0 missing
SM04_EA.dm.numeric43 unique values
0 missing
SM06_EA.dm.numeric42 unique values
0 missing
SpMAD_EA.dm.numeric77 unique values
0 missing
X3Anumeric25 unique values
0 missing
SpMin7_Bh.p.numeric71 unique values
0 missing
SpDiam_AEA.ed.numeric63 unique values
0 missing
ARRnumeric37 unique values
0 missing
BACnumeric53 unique values
0 missing
P_VSA_LogP_6numeric10 unique values
0 missing
Eig02_EA.ri.numeric39 unique values
0 missing
PW5numeric27 unique values
0 missing
SssNHnumeric100 unique values
0 missing
C.033numeric2 unique values
0 missing
SpMax7_Bh.e.numeric89 unique values
0 missing
SpMax7_Bh.m.numeric93 unique values
0 missing
SpMax7_Bh.v.numeric97 unique values
0 missing
ATSC5enumeric112 unique values
0 missing
ATSC6enumeric110 unique values
0 missing
Chi0_EA.dm.numeric77 unique values
0 missing
Eig04_AEA.dm.numeric72 unique values
0 missing
Eig05_AEA.dm.numeric58 unique values
0 missing
Eig05_AEA.ri.numeric71 unique values
0 missing
Eig05_EAnumeric58 unique values
0 missing
Eig13_EAnumeric65 unique values
0 missing
Eig13_EA.bo.numeric78 unique values
0 missing
Eig13_EA.ed.numeric56 unique values
0 missing
Eig13_EA.ri.numeric80 unique values
0 missing
Eig14_AEA.dm.numeric66 unique values
0 missing
Eig14_EAnumeric66 unique values
0 missing
Eig14_EA.bo.numeric84 unique values
0 missing
Eig14_EA.ed.numeric64 unique values
0 missing
Eig14_EA.ri.numeric77 unique values
0 missing
Eig15_AEA.dm.numeric63 unique values
0 missing
Eig15_EAnumeric61 unique values
0 missing
Eig15_EA.bo.numeric84 unique values
0 missing
Eig15_EA.ed.numeric68 unique values
0 missing
Eig15_EA.ri.numeric78 unique values
0 missing
GATS4enumeric101 unique values
0 missing
GATS4snumeric106 unique values
0 missing
nHAccnumeric17 unique values
0 missing
nHetnumeric17 unique values
0 missing
Psi_e_Anumeric89 unique values
0 missing
Psi_i_Anumeric89 unique values
0 missing

62 properties

119
Number of instances (rows) of the dataset.
65
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.
64
Number of numeric attributes.
1
Number of nominal attributes.
2.77
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.45
Third quartile of skewness among attributes of the numeric type.
81.51
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.28
First quartile of kurtosis among attributes of the numeric type.
1.41
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.
0.65
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
5.52
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.
14.83
Mean of means among attributes of the numeric type.
-1.17
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.2
First quartile of standard deviation of attributes of the numeric type.
-0.06
Average class difference between consecutive instances.
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.
Entropy of the target attribute values.
Average number of distinct values among the attributes of the nominal type.
1.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.55
Number of attributes divided by the number of instances.
-0.47
Mean skewness among attributes of the numeric type.
1.38
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.
Percentage of instances belonging to the most frequent class.
5.09
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.
-0.22
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.04
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.5
Second quartile (Median) of standard deviation of attributes of the numeric type.
79.06
Maximum kurtosis among attributes of the numeric type.
-0.06
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
202.13
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.
3.76
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.
98.46
Percentage of numeric attributes.
4.11
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-7.94
Minimum skewness among attributes of the numeric type.
1.54
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.

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