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QSAR鱼类毒性数据集

QSAR鱼类毒性数据集

Scene:

Physical

Data Type:

Regression
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    Data Structure ?

    *数据结构实际以真实数据为准

    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. LC50 data, which is the concentration that causes death in 50% of test fish over a test duration of 96 hours, was used as model response. The model comprised 6 molecular descriptors: MLOGP (molecular properties), CIC0 (information indices), GATS1i (2D autocorrelations), NdssC (atom-type counts), NdsCH ((atom-type counts), SM1_Dz(Z) (2D matrix-based descriptors). Details can be found in the quoted reference: M. Cassotti, D. Ballabio, R. Todeschini, V. Consonni. A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas), SAR and QSAR in Environmental Research (2015), 26, 217-243; doi: 10.1080/1062936X.2015.1018938


    Attribute Information:

    6 molecular descriptors and 1 quantitative experimental response:
    1) CIC0
    2) SM1_Dz(Z)
    3) GATS1i
    4) NdsCH
    5) NdssC
    6) MLOGP
    7) quantitative response, LC50 [-LOG(mol/L)]


    Relevant Papers:

    M. Cassotti, D. Ballabio, R. Todeschini, V. Consonni. A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas), SAR and QSAR in Environmental Research (2015), 26, 217-243; doi: 10.1080/1062936X.2015.1018938



    Citation Request:

    Please, cite the following paper if you publish results based on the QSAR fish toxicity dataset: M. Cassotti, D. Ballabio, R. Todeschini, V. Consonni. A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas), SAR and QSAR in Environmental Research (2015), 26, 217-243; doi: 10.1080/1062936X.2015.1018938

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