Sakkiah Sugunadevi, Selvaraj Chandrabose, Gong Ping, Zhang Chaoyang, Tong Weida, Hong Huixiao
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.
Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.
Oncotarget. 2017 Oct 10;8(54):92989-93000. doi: 10.18632/oncotarget.21723. eCollection 2017 Nov 3.
We developed an ER binding prediction model to facilitate identification of chemicals specifically bind ER or ER together with our previously developed ER binding model. Decision Forest was used to train ER 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 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 binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER or ER.
我们开发了一种雌激素受体（ER）结合预测模型，以促进对与ER特异性结合的化学物质的识别，或结合我们之前开发的ER结合模型来识别与ER结合的化学物质。决策森林用于基于从雌激素活性数据库（EADB）获得的大量化合物训练ER结合预测模型。通过5折交叉验证的1000次迭代来评估模型性能。使用交叉验证的预测来分析预测置信度。通过分析5折交叉验证中模型使用的化学描述符的频率数据，确定了对ER结合有信息价值的化学特征。进行了1000次排列以评估偶然相关性。5折交叉验证的平均准确率为93.14%，标准差为0.64%。预测置信度分析表明，预测置信度越高，预测就越准确。排列检验结果表明，该预测模型不太可能是偶然产生的。确定了18个有信息价值的描述符对ER结合预测很重要。将该预测模型应用于ToxCast项目的数据，得到了90 - 92%的非常高的灵敏度。我们的结果表明，使用所开发的模型可以准确预测化学物质与ER的结合。结合我们之前开发的ER预测模型，预计该模型可通过识别与ER特异性结合的化学物质来促进药物开发。
