用机器学习和分子动力学模拟来选潜在的双A替代品对雄激素受体的对抗性活动
Zeguo Yang1, Ling Wang1, Ying Yang1
1Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
双甲 (BPA) 替代品使用新型深度学习模型选了雄激素受体 (AR) 反对作用. 由于预测的非对抗性活性,由素衍生 bisguaiacols 显示出作为更安全的替代品的承诺.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
背景情况:
- 双甲 (BPA) 暴露对健康构成风险,因为它通过与雄激素受体 (AR) 结合而起作用作为内分泌干扰剂.
- 许多BPA替代品正在成为环境污染物,需要进行系统的安全评估.
- 了解结构-活动关系对于开发更安全的BPA替代品至关重要.
研究的目的:
- 开发一种高通量选方法,用于评估BPA替代品的AR抗活性.
- 使用混合深度学习模型预测BPA替代品的安全性.
- 阐明BPA及其类型的AR抗体的分子机制.
主要方法:
- 开发了一种混合深度学习架构,将分子描述符和图形结合起来,以预测AR对抗性活动.
- 使用高通量虚拟选来评估一系列BPA替代品.
- 用分子动力学 (MD) 模拟来调查与AR抗性相关的结合部位和形状变化.
主要成果:
- 混合深度学习模型证明了在BPA替代品中预测AR对抗性的改进的概括能力.
- 据预测,由林格宁衍生的bisguaiacols是AR的非对手,与一些双相似物不同.
- 医学模拟确定了二测试结合的口袋作为双相似物的主要结合部位,并解释了AR对抗的机制.
结论:
- 开发的计算方法有助于对BPA替代品进行毒理学查.
- 素衍生 bisguaiacols 代表了一个有前途的环保替代 BPA 的替代品.
- 这项研究为设计具有减少内分泌干扰潜力的更安全化学品提供了基础.
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