Development of estrogen receptor beta binding prediction model using large sets of chemicals

We developed an ER(β) binding prediction model to facilitate identification of chemicals specifically bind ER(β) or ER(α) together with our previously developed ER(α) binding model. Decision Forest was used to train ER(β) binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER(β) binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER(β) binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER(β) binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER(α) prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER(β) or ER(α).