Combining docking and comparative molecular similarity indices analysis (COMSIA) to predict estrogen activity and probe molecular mechanisms of estrogen activity for estrogen compounds

Estrogen compounds are suspected of disrupting endocrine functions by mimicking natural hormones, and such compounds may pose a serious threat to the health of humans and wildlife. Close attention has been paid to the prediction and molecular mechanisms of estrogen activity for estrogen compounds. In this article, estrogen receptor α subtype (ERα)–based comparative molecular similarity indices analysis (COMSIA) was performed on 44 estrogen compounds with structural diversity to find out the structural relationship with the activity and to predict the activity. The model with the significant correlation and the best predictive power (R2 = 0.965, Q2LOO = 0.599, R2pred = 0.825) was achieved. The COMSIA and docking results revealed the structural features for estrogen activity and key amino acid residues in binding pocket, and provided an insight into the interaction between the ligands and these amino acid residues.

Estrogen compounds are suspected of disrupting endocrine functions by mimicking natural hormones, and such compounds may pose a serious threat to the health of humans and wildlife. Close attention has been paid to the prediction and molecular mechanisms of estrogen activity for estrogen com- pounds. In this article, estrogen receptor a subtype （ERa） -based comparative molecular similarity indices analysis （COMSIA） was performed on 44 estrogen compounds with structural diversity to find out the structural relationship with the activity and to predict the activity. The model with the significant correlation and the best predictive power （R^2= 0.965, Q^2 LOO： 0.599, R^2 pred ： 0.825） was achieved. The COMSIA and docking results revealed the structural features for estrogen activity and key amino acid residues in binding pocket, and provided an insight into the interaction between the ligands and these amino acid residues.