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- W3156324074 abstract "The present study analyzed the lethal dose (LD50) of 73 hydrazine compounds in rats and established a comprehensive toxicity database of hydrazine compounds. Based on the existing toxicity database, the 1D–3D molecular descriptors of hydrazine compounds were obtained by quantum chemistry calculations and using DRAGON 6.0 software, and subsequently a quantitative structure–activity relationship prediction model was constructed. Aiming at the existing defects of the traditional neural network, such as easy to fall into local optimum and limited by the number of data samples, this paper proposed an improved BP neural network to simulate the toxicity prediction of hydrazine compounds. The determination coefficient and error coefficient of the prediction data set were found to be 0.864 and 0.679, respectively. The simulation results showed that the improved neural network model offered better prediction results for the toxicity of hydrazine compounds, which met the actual accuracy requirements for the toxicity prediction for hydrazine compounds." @default.
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- W3156324074 date "2021-05-01" @default.
- W3156324074 modified "2023-10-03" @default.
- W3156324074 title "A novel toxicity prediction model for hydrazine compounds based on 1D–3D molecular descriptors" @default.
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- W3156324074 doi "https://doi.org/10.1016/j.comtox.2021.100169" @default.
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