Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees.

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第一作者： Mainak,Chatterjee

第一单位： Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

作者： Mainak,Chatterjee ^[1] ; Arkaprava,Banerjee ^[1] ; Simone,Tosi ^[2] ; Edoardo,Carnesecchi ^[3] ; Emilio,Benfenati ^[4] ; Kunal,Roy ^[5]

作者单位： Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India. ^[1] Department of Agricultural, Forest, and Food Sciences, University of Turin, Turin, Italy. ^[2] Institute for Risk Assessment Sciences, Utrecht, the Netherlands. ^[3] Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, via Mario Negri 2, 20156 Milano, Italy. ^[4] Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India. Electronic address: kunalroy_in@yahoo.com. ^[5]

关键词 Artificial intelligence Bees Ecotoxicity Mixing rules Predictability Q-RASAR

主题词蜂(Bees);动物(Animals);量化构效关系(Quantitative Structure-Activity Relationship);结果可重复性(Reproducibility of Results);算法(Algorithms);农药(Pesticides)

DOI 10.1016/j.jhazmat.2023.132358

PMID 37634379

发布时间 2023-09-20

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