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A Quantitative Structure−Activity Relationships Model for the Acute Toxicity of Substituted Benzenes to Tetrahymena pyriformis Using Bayesian-Regularized Neural Networks
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inferredcites →https://doi.org/10.1002/qsar.19960150103openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9781119681397.ch11openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cmdc.201000056openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/etc.5620220319openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200390006openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200390010openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200530172openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200760167openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4419-0197-2_1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4939-7899-1_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-60327-118-9_27openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00204-015-1587-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00894-005-0014-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-005-9025-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11270-005-9068-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11426-008-0076-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s13738-023-02859-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-12-801238-3.05329-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-36474-7.00006-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-008055232-3.60006-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.aquatox.2016.09.006openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bbe.2014.03.003openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bmc.2004.11.014openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bmc.2004.12.028openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bmc.2005.08.009openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bmc.2005.12.048openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.chemolab.2010.08.007openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.chemolab.2010.08.018openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.chemosphere.2008.03.026openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ecoenv.2013.11.019openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.etap.2016.01.010openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jag.2017.02.013openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jmgm.2004.03.010openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jmgm.2005.03.001openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jmgm.2009.12.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/s0166-1280(02)00620-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/s1741-8364(04)02402-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00261openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci000450aopenalex-snapshot
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inferred← citeshttps://doi.org/10.1080/17460441.2016.1201262openalex-snapshot
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inferred← citeshttps://doi.org/10.1089/ees.2015.0164openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iconip.2002.1202872openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ijcnn.2001.938759openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/j.2042-7158.1958.tb10331.xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1142/s0219720003000216openalex-snapshot
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inferred← citeshttps://doi.org/10.1201/9781420028836.ch28openalex-snapshot
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inferred← citeshttps://doi.org/10.1360/zb2008-38-6-461openalex-snapshot
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inferred← citeshttps://doi.org/10.1385/mb:27:2:139openalex-snapshot
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inferred← citeshttps://doi.org/10.1533/9781855738379.2.245openalex-snapshot
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inferred← citeshttps://doi.org/10.1897/01-234openalex-snapshot
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inferred← citeshttps://doi.org/10.2166/wst.2020.440openalex-snapshot
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inferred← citeshttps://doi.org/10.2174/1386207318666141229125155openalex-snapshot
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inferred← citeshttps://doi.org/10.2477/jccj.1.23openalex-snapshot
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inferred← citeshttps://doi.org/10.2477/jccj.4.89openalex-snapshot
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