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QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4674

QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4674

deactivated ARFF Publicly available Visibility: public Uploaded 14-07-2016 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: CHEMBL4674 (TID: 12983), and it has 810 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)numeric43 unique values
0 missing
molecule_id (row identifier)nominal810 unique values
0 missing
Chi0_EA.dm.numeric693 unique values
0 missing
Chi1_EA.dm.numeric703 unique values
0 missing
MATS1vnumeric174 unique values
0 missing
MATS1inumeric388 unique values
0 missing
ATSC5enumeric533 unique values
0 missing
Eig04_EA.dm.numeric49 unique values
0 missing
Eig02_EA.bo.numeric455 unique values
0 missing
SM12_AEA.ri.numeric455 unique values
0 missing
SssOnumeric266 unique values
0 missing
NssOnumeric5 unique values
0 missing
AACnumeric416 unique values
0 missing
AECCnumeric580 unique values
0 missing
ALOGPnumeric714 unique values
0 missing
ALOGP2numeric769 unique values
0 missing
AMRnumeric774 unique values
0 missing
AMWnumeric627 unique values
0 missing
ARRnumeric197 unique values
0 missing
ATS1enumeric522 unique values
0 missing
ATS1inumeric523 unique values
0 missing
ATS1mnumeric506 unique values
0 missing
ATS1pnumeric500 unique values
0 missing
ATS1snumeric515 unique values
0 missing
ATS1vnumeric500 unique values
0 missing
ATS2enumeric540 unique values
0 missing
ATS2inumeric561 unique values
0 missing
ATS2mnumeric519 unique values
0 missing
ATS2pnumeric528 unique values
0 missing
ATS2snumeric575 unique values
0 missing
ATS2vnumeric520 unique values
0 missing
ATS3enumeric539 unique values
0 missing
ATS3inumeric564 unique values
0 missing
ATS3mnumeric539 unique values
0 missing
ATS3pnumeric543 unique values
0 missing
ATS3snumeric555 unique values
0 missing
ATS3vnumeric537 unique values
0 missing
ATS4enumeric593 unique values
0 missing
ATS4inumeric591 unique values
0 missing
ATS4mnumeric580 unique values
0 missing
ATS4pnumeric583 unique values
0 missing
ATS4snumeric582 unique values
0 missing
ATS4vnumeric562 unique values
0 missing
ATS5enumeric600 unique values
0 missing
ATS5inumeric598 unique values
0 missing
ATS5mnumeric591 unique values
0 missing
ATS5pnumeric601 unique values
0 missing
ATS5snumeric600 unique values
0 missing
ATS5vnumeric606 unique values
0 missing
ATS6enumeric613 unique values
0 missing
ATS6inumeric624 unique values
0 missing
ATS6mnumeric601 unique values
0 missing
ATS6pnumeric610 unique values
0 missing
ATS6snumeric608 unique values
0 missing
ATS6vnumeric620 unique values
0 missing
ATS7enumeric639 unique values
0 missing
ATS7inumeric642 unique values
0 missing
ATS7mnumeric632 unique values
0 missing
ATS7pnumeric638 unique values
0 missing
ATS7snumeric637 unique values
0 missing
ATS7vnumeric628 unique values
0 missing
ATS8enumeric644 unique values
0 missing
ATS8inumeric666 unique values
0 missing
ATS8mnumeric636 unique values
0 missing
ATS8pnumeric657 unique values
0 missing
ATS8snumeric644 unique values
0 missing
ATS8vnumeric644 unique values
0 missing
ATSC1enumeric228 unique values
0 missing
ATSC1inumeric468 unique values
0 missing
ATSC1mnumeric737 unique values
0 missing
ATSC1pnumeric701 unique values
0 missing

62 properties

810
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.
Average entropy of the attributes.
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.
2.62
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.
6.03
Mean of means among attributes of the numeric type.
-0.8
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.27
First quartile of standard deviation of attributes of the numeric type.
0.79
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.
0.83
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.09
Number of attributes divided by the number of instances.
-0.17
Mean skewness among attributes of the numeric type.
3.96
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.
1.3
Mean standard deviation of 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.
Minimal entropy among attributes.
-0.19
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
-0.16
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.36
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
17.59
Maximum 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.
105.61
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.
4.63
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.
98.59
Percentage of numeric attributes.
4.85
Third quartile of means among attributes of the numeric type.
The maximum number of distinct values among attributes of the nominal type.
-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.
3.78
Maximum skewness among attributes of the numeric type.
0.04
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.
24.18
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.18
First quartile of kurtosis among attributes of the numeric type.
0.55
Third 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
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