QSAR 水生物毒性数据集

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