The bi-directional selective low toxicity/high flame retardancy organophosphorus fire retardants (OPFRs) derivatives were designed by a comprehensive effect 3D quantitative structure-activity relationship (QSAR) pharmacophore model, and the toxicity and flame retardancy mechanism of OPFR derivatives were explored. The 3D-QSAR comprehensive pharmacophore model was constructed using the toxicity/flame retardancy comprehensive evaluation values of OPFRs for molecular modifications, which were obtained by the Mamdani fuzzy inference approach. The environment-friendly OPFR derivatives (CDPP-F, CDPP-NO2, TPHP-F, TDCIPP-CH2CH3, and TDCIPP-Br) with high flame retardancy showed significantly reduced multi-toxicity effects (biotoxicity, reproductive toxicity, and neurotoxicity) in the comprehensive model. The spatial overlapping volumes of the toxicity/flame retardancy comprehensive effect model with the toxic effect and with flame retardant effect were 1 : 1. The trend (1 : 1) was similar to the degree of improvement of toxicity and flame retardancy of the OPFR derivatives. The toxicity and flame retardancy were decreased by more than 50%. This indicated that the spatial overlapping volumes in the comprehensive model with the toxic and flame retardant mono-models have significant effects. Based on the 2D-QSAR model, molecular docking, and density functional theory, it was found that, in molecular modification, the introduction of electronegative groups to improve the electronic parameters (q+) can reduce the toxicity of OPFRs. An increase in the bond length and bond angle of the molecular side chain increased the steric parameter (MR) that improved the molecular flame retardancy of OPFRs.
Modified organophosphorus fire retardant with low toxicity/high flame retardancy using the pharmacophore model associated with Mamdani fuzzy inference approach
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Jiawen Yang, Yilin Hou, Qing Li, Yu Li; Modified organophosphorus fire retardant with low toxicity/high flame retardancy using the pharmacophore model associated with Mamdani fuzzy inference approach. Biochem J 11 December 2020; 477 (23): 4655–4674. doi: https://doi.org/10.1042/BCJ20200779
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