结合高通量选和模拟来降低风险的新型农业化学品对雄激素受体结合
Martin Adamczewski1, Britta Nisius1, Nina Kausch-Busies1
1Bayer AG, Crop Science, Alfred-Nobel-Str. 50, Monheim 40789, Germany.
Chemical research in toxicology
|March 13, 2026
概括
这项研究选了超过72,000种农业化学品与雄激素受体 (AR) 结合,确定了数千种结合剂. 使用这些数据开发了机器学习模型,用于预测化学安全评估的AR调制.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学是一种计算化学.
背景情况:
- 由于内分泌干扰,雄激素受体 (AR) 调节是环境化学品的关键安全问题.
- 现有的AR调制公共数据缺乏足够的规模和多样性,无法进行可靠的分析.
研究的目的:
- 对农业化学品进行AR结合的大规模高通量选 (HTS).
- 开发和验证用于预测AR绑定活动的机器学习模型.
- 为AR调制研究创建一个全面的,公开可用的数据集.
主要方法:
- 使用光极化位移测定用于超过72,000种农业化学化合物的HTS.
- 进行了确认剂量反应测试,以确定AR结合剂.
- 策划了24953个化合物的数据集与活动数据,并使用分子描述器训练了机器学习模型 (梯度增强树).
主要成果:
- 确定了4,183个AR结合剂 (5.7%的命中率),具有显著的结构多样性.
- 开发了具有0.77的平衡精度和0.98的NPV的机器学习模型,用于预测AR绑定器.
- 实现了模型与外部数据集的合理可转移性 (平衡精度为0.66-0.72).
结论:
- 与机器学习相结合的HTS对于早期化学安全评估是有效的.
- 该研究提供了一个有价值的基准数据集,以改善AR绑定预测.
- 开发的模型可以帮助消除AR调制潜力的大型虚拟化学库的风险.
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