Data
qsar_aquatic_toxicity

qsar_aquatic_toxicity

active ARFF CC BY 4.0 Visibility: public Uploaded 23-07-2024 by Bruno Belucci Teixeira
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From original source: ----- Data set containing values for 8 attributes (molecular descriptors) of 546 chemicals used to predict quantitative acute aquatic toxicity towards Daphnia Magna.. Additional Information This dataset was used to develop quantitative regression QSAR models to predict acute aquatic toxicity towards the fish Pimephales promelas (fathead minnow) on a set of 908 chemicals. to predict acute aquatic toxicity towards Daphnia Magna. LC50 data, which is the concentration that causes death in 50% of test D. magna over a test duration of 48 hours, was used as model response. The model comprised 8 molecular descriptors: TPSA(Tot) (Molecular properties), SAacc (Molecular properties), H-050 (Atom-centred fragments), MLOGP (Molecular properties), RDCHI (Connectivity indices), GATS1p (2D autocorrelations), nN (Constitutional indices), C-040 (Atom-centred fragments). Details can be found in the quoted reference: M. Cassotti, D. Ballabio, V. Consonni, A. Mauri, I. V. Tetko, R. Todeschini (2014). Prediction of acute aquatic toxicity towards daphnia magna using GA-kNN method, Alternatives to Laboratory Animals (ATLA), 42,31:41; doi: 10.1177/026119291404200106 Has Missing Values? No -----

9 features

8 (target)numeric515 unique values
0 missing
0numeric227 unique values
0 missing
1numeric210 unique values
0 missing
2numeric11 unique values
0 missing
3numeric405 unique values
0 missing
4numeric342 unique values
0 missing
5numeric403 unique values
0 missing
6numeric9 unique values
0 missing
7numeric6 unique values
0 missing

19 properties

546
Number of instances (rows) of the dataset.
9
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.
9
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-0.45
Average class difference between consecutive instances.
0
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
100
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.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: 8
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