Asako Yuki, Uesawa Yoshihiro
Department of Clinical Pharmaceutics Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan.
Molecules. 2017 Apr 23;22(4):675. doi: 10.3390/molecules22040675.
Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. Owing to the correlation between our model's and the challenge's results, we consider that our model currently possesses the highest predictive power on agonist activity of the estrogen receptor ligand-binding domain. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.
已知许多雌激素受体激动剂会扰乱内分泌功能。我们开发了一种计算模型，可在检测系统中预测雌激素受体配体结合域的激动剂。我们的模型参加了2014年Tox21数据挑战赛，这是由美国国立转化医学推进中心组织的一场计算毒理学竞赛。该竞赛旨在为包括雌激素受体在内的各种不良结局途径找到高性能的预测模型。我们基于随机森林方法的预测模型在其竞赛类别中表现最佳。在当前研究中，通过严格调整超参数以避免过度拟合，提高了随机森林模型的预测性能。随机森林模型是从同时应用于10000个雌激素受体配体结合域活性检测结果的4000个描述符中进行优化的，这些结果由Tox21测量和汇编。由于我们模型的结果与挑战赛结果之间存在相关性，我们认为我们的模型目前对雌激素受体配体结合域的激动剂活性具有最高的预测能力。此外，对优化模型的分析揭示了激动剂的一些重要特征，例如分子中的羟基数量。
