Abstract
In the present study, the predication of the binding affinity (log RBA) of estrogen receptor alpha with three categories of environmental endocrine disrupting chemicals (EDCs), namely, PCB, phenol, and DDT, is performed by the quantum chemical genetic algorithm multiple linear regression (GA-MLR) method. The result of the optimal model indicates that log RBA increases with increasing the electrophilicity and hydrophobicity of EDCs. However, by using the quantum chemical cluster model approach, the modeling results reveal that electrostatic interaction and hydrogen bonding play a significant role. The chemical reactivity descriptors calculated based on the conceptual density functional theory also indicate that the binding mechanism of charge-controlled interaction is superior to that of frontier-controlled interaction.
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