Data
QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2007

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2007

deactivated ARFF Publicly available Visibility: public Uploaded 15-07-2016 by Noureddin Sadawi
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This dataset contains QSAR data (from ChEMBL version 17) showing activity values (unit is pseudo-pCI50) of several compounds on drug target ChEMBL_ID: CHEMBL2007 (TID: 12627), and it has 612 rows and 69 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.

71 features

pXC50 (target)numeric226 unique values
0 missing
molecule_id (row identifier)nominal612 unique values
0 missing
SssOnumeric251 unique values
0 missing
Vindexnumeric200 unique values
0 missing
Xindexnumeric228 unique values
0 missing
Chi0_EA.dm.numeric544 unique values
0 missing
nArORnumeric5 unique values
0 missing
SpMin3_Bh.i.numeric321 unique values
0 missing
O.060numeric5 unique values
0 missing
Eig07_EA.bo.numeric411 unique values
0 missing
SpMin3_Bh.e.numeric322 unique values
0 missing
X1solnumeric487 unique values
0 missing
SpMax3_Bh.e.numeric314 unique values
0 missing
IDETnumeric536 unique values
0 missing
SpMax3_Bh.i.numeric299 unique values
0 missing
P_VSA_MR_1numeric88 unique values
0 missing
Xunumeric532 unique values
0 missing
SpMax3_Bh.p.numeric338 unique values
0 missing
SpMax3_Bh.v.numeric337 unique values
0 missing
LPRSnumeric537 unique values
0 missing
Chi0_EA.ri.numeric569 unique values
0 missing
MSDnumeric517 unique values
0 missing
AECCnumeric472 unique values
0 missing
X5numeric510 unique values
0 missing
X1numeric460 unique values
0 missing
Psi_e_0numeric562 unique values
0 missing
S1Knumeric493 unique values
0 missing
Chi0_EA.bo.numeric518 unique values
0 missing
ON1numeric220 unique values
0 missing
IDMTnumeric536 unique values
0 missing
Eig05_EAnumeric407 unique values
0 missing
SM13_AEA.bo.numeric407 unique values
0 missing
IDDMnumeric301 unique values
0 missing
NssOnumeric6 unique values
0 missing
ATS7vnumeric493 unique values
0 missing
SpMaxA_EA.bo.numeric150 unique values
0 missing
SpMaxA_AEA.ed.numeric197 unique values
0 missing
SAtotnumeric561 unique values
0 missing
Chi0_AEA.bo.numeric453 unique values
0 missing
Chi0_AEA.dm.numeric453 unique values
0 missing
Chi0_AEA.ed.numeric453 unique values
0 missing
Chi0_AEA.ri.numeric453 unique values
0 missing
Chi0_EAnumeric453 unique values
0 missing
SpMax2_Bh.i.numeric240 unique values
0 missing
SpMaxA_AEA.ri.numeric149 unique values
0 missing
IDEnumeric442 unique values
0 missing
CENTnumeric477 unique values
0 missing
CIDnumeric354 unique values
0 missing
Chi0_EA.ed.numeric521 unique values
0 missing
IVDMnumeric254 unique values
0 missing
ICRnumeric389 unique values
0 missing
Eta_alphanumeric411 unique values
0 missing
VvdwMGnumeric516 unique values
0 missing
Vxnumeric516 unique values
0 missing
SpMaxA_EAnumeric125 unique values
0 missing
P_VSA_s_4numeric420 unique values
0 missing
XMODnumeric567 unique values
0 missing
X0solnumeric394 unique values
0 missing
ECCnumeric359 unique values
0 missing
SpAD_AEA.bo.numeric545 unique values
0 missing
ATS1pnumeric429 unique values
0 missing
Chi1_EA.ri.numeric582 unique values
0 missing
S2Knumeric528 unique values
0 missing
X2numeric518 unique values
0 missing
Chi1_EA.ed.numeric509 unique values
0 missing
X0vnumeric550 unique values
0 missing
CSInumeric445 unique values
0 missing
Eig05_AEA.ri.numeric427 unique values
0 missing
MDDDnumeric530 unique values
0 missing
ATS8pnumeric496 unique values
0 missing
Chi1_AEA.bo.numeric500 unique values
0 missing

62 properties

612
Number of instances (rows) of the dataset.
71
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.
70
Number of numeric attributes.
1
Number of nominal attributes.
-0.12
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.12
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
9.7
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.
-0.28
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.
287.7
Mean standard deviation of attributes of the numeric type.
-0.32
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.
2.75
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.68
Minimum kurtosis among attributes of the numeric type.
0.11
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
10.76
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
2.36
Third quartile of kurtosis among attributes of the numeric type.
25423.66
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
98.59
Percentage of numeric attributes.
20.41
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-2.52
Minimum skewness among attributes of the numeric type.
1.41
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.
0.03
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.28
Third quartile of skewness among attributes of the numeric type.
1.96
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.37
First quartile of kurtosis among attributes of the numeric type.
5.56
Third quartile of standard deviation of attributes of the numeric type.
17064.02
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
3.6
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.
1.26
Mean kurtosis among attributes of the numeric type.
-0.89
First quartile of skewness among attributes of the numeric type.
460.09
Mean of means among attributes of the numeric type.
0.37
First quartile of standard deviation of attributes of the numeric type.
-0.02
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
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