可解读的机器学习用于识别雌激素受体激动剂,抗剂和结合剂
Geven Piir1, Sulev Sild1, Uko Maran1
1Institute of Chemistry, University of Tartu, Ravila 14A, Tartu, 50411, Estonia.
Chemosphere
|November 11, 2023
概括
这项研究开发了机器学习模型,通过分析雌激素受体 (ER) 活性和化学结构来识别内分泌干扰化学物质 (EDC). 这些模型预测化学相互作用,有助于危险评估和减少动物试验.
科学领域:
- 毒理学和化学信息学
- 计算毒理学计算毒理学
- 内分泌干扰研究研究 内分泌干扰研究
背景情况:
- 内分泌干扰化学物质 (EDC) 干扰荷尔蒙活动,导致癌症和肥胖等不良健康结果.
- 由EDCs破坏雌激素信号传递是一个重大问题,需要识别危险化学品的方法.
- 新方法方法 (NAM),包括计算模型,为化学危险评估提供了动物试验的替代方案.
研究的目的:
- 开发和验证用于预测与雌激素受体 (ER) 的化学相互作用的机器学习模型.
- 使用计算方法识别作为ER结合剂,激动剂或对抗剂的化学物质.
- 提供一种具有成本效益和快速的方法,用于对潜在的EDC进行初步危险评估.
主要方法:
- 利用了来自ToxCast/Tox21项目的综合体内测试数据.
- 开发了随机森林分类模型来预测ER结合,激进和对抗性活动.
- 采用理论分子描述符和类概率用于模型解释和适用性领域分析.
主要成果:
- 对于ER结合,激动剂和对抗剂,达到高达82%的分类预测准确度.
- 模型的解释与ER连接体结合口袋内的已知分子相互作用保持一致.
- 开发的模型证明了对降低化学品优先级和减少动物试验的可靠性,特别是在不平衡数据的情况下.
结论:
- 机器学习模型有效地预测雌激素受体活性,并识别潜在的EDC.
- 这些NAM为初步化学危险评估提供了宝贵的工具,支持监管决策.
- 在QsarDB.org上公开访问的模型遵循FAIR原则,促进可重复使用性和协作.
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