Data
QSAR-Bioconcentration-Classes-Data-Set

QSAR-Bioconcentration-Classes-Data-Set

active ARFF Database: Open Database, Contents: Original Authors Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Data Set Information: A dataset of manually-curated BCF for 779 chemicals was used to determine the mechanisms of bioconcentration, i.e. to predict whether a chemical: (1) is mainly stored within lipid tissues, (2) has additional storage sites (e.g. proteins), or (3) is metabolized/eliminated. Data were randomly split into a training set of 584 compounds (75) and a test set of 195 compounds (25), preserving the proportion between the classes. Two QSAR classification trees were developed using CART (Classification and Regression Trees) machine learning technique coupled with Genetic Algorithms. The file contains the selected Dragon descriptors (9) along with CAS, SMILES, experimental BCF, experimental/predicted KOW and mechanistic class (1, 2, 3). Further details on model development and performance along with descriptor definitions and interpretation are provided in the original manuscript (Grisoni et al., 2016). Attribute Information: 3 Compound identifiers: CAS number Molecular SMILES Train/test splitting 9 molecular descriptors (independent variables) nHM piPC09 PCD X2Av MLOGP ON1V N-072 B02[C-N] F04[C-O] 2 experimental responses: Bioconcentration Factor (BCF) in log units (regression) Bioaccumulation class (three classes) Relevant Papers: F. Grisoni, V.Consonni, M.Vighi, S.Villa, R.Todeschini (2016). Investigating the mechanisms of bioconcentration through QSAR classification trees, Environment International, 88, 198-205

13 features

CASstring779 unique values
0 missing
SMILESstring779 unique values
0 missing
Set (ignore)string2 unique values
0 missing
nHMnumeric11 unique values
0 missing
piPC09numeric322 unique values
0 missing
PCDnumeric224 unique values
0 missing
X2Avnumeric63 unique values
0 missing
MLOGPnumeric346 unique values
0 missing
ON1Vnumeric261 unique values
0 missing
N-072numeric4 unique values
0 missing
B02[C-N]numeric2 unique values
0 missing
F04[C-O]numeric23 unique values
0 missing
Classnumeric3 unique values
0 missing
logBCFnumeric391 unique values
0 missing

19 properties

779
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
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.
11
Number of numeric attributes.
0
Number of nominal attributes.
0.02
Number of attributes divided by the number of instances.
84.62
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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