基于对抗传播的人工神经网络的雄激素和雌激素受体化合物结合的预测模型
Mark Stanojević1,2, Marija Sollner Dolenc2, Marjan Vračko3
1BiSafe d.o.o., 1000 Ljubljana, Slovenia.
Toxics
|June 27, 2023
概括
人工神经网络 (ANN) 可以准确地预测内分泌干扰化学物质 (EDC) 如何与雄激素受体 (AR) 和雌激素受体 (ER) 结合. 这些模型提供了一个强大的工具,用于识别和优先考虑潜在的有害化学品,仅基于结构.
科学领域:
- 内分泌学 在内分泌学.
- 计算化学的计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 内分泌干扰化学物质 (EDC) 干扰人类内分泌系统.
- EDCs可以调节核受体的活动,如雄激素受体 (ARs) 和雌激素受体 (ERα,ERβ).
- 识别和减少对EDC的暴露对公共健康至关重要.
研究的目的:
- 开发和验证人工神经网络 (ANN) 模型,用于预测与AR和ER的化学结合.
- 评估对抗传播人工神经网络 (CPANN) 对于EDC查的有用性.
- 为进一步的安全评估提供基于结构的方法来优先考虑化学品.
主要方法:
- 开发了六种对抗传播的人工神经网络 (CPANN) 模型.
- 训练模型使用来自CompTox化学品仪表板的各种化学结构和活性数据数据集.
- 使用交叉验证 (LOO) 验证的模型性能.
主要成果:
- 模型实现了极好的预测准确度,从94%到100%不等.
- 开发的模型准确地预测了化合物与ARs,ERα和ERβ作为激动剂或对抗剂的结合.
- 预测仅基于该化合物的化学结构.
结论:
- ANN模型提供了一种可靠的方法来预测EDC与关键核受体的相互作用.
- 这些模型是化学品早期安全优先级的宝贵工具.
- 基于结构的预测减少了对EDC进行广泛实验查的需求.
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