Davide Ballabio (davide.ballabio '@' unimib.it), Matteo Cassotti, Viviana Consonni, Roberto Todeschini, Milano Chemometrics and QSAR Research Group (http://www.michem.unimib.it/), Universit?? degli Studi Milano - Bicocca, Milano (Italy)

Data Set 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

Attribute Information:

8 molecular descriptors and 1 quantitative experimental response:
1) TPSA(Tot)
2) SAacc
3) H-050
4) MLOGP
5) RDCHI
6) GATS1p
7) nN
8) C-040
9) quantitative response, LC50 [-LOG(mol/L)]

Relevant Papers:

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

Citation Request:

Please, cite the following paper if you publish results based on the QSAR aquatic toxicity dataset: 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