基于图形的多模式交叉注意力框架,用于预测活体内分泌干扰物
Eder Soares de Almeida Santos1, Gustavo Felizardo Santos Sandes1, Artur Christian Garcia da Silva2
1Laboratory of Cheminformatics, Faculty of Pharmacy, Universidade Federal de Goiás, Goiás, Brazil.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 15, 2026
概括
这项研究引入了用于内分泌危害评估的新AI框架,使用分子数据和生物通路信息准确预测生物体水平的结果. 该模型为监管毒理学提供透明,机械可解释的结果.
科学领域:
- 计算毒理学和化学信息学
- 内分泌干扰和危险评估
- 不良结果路径 (AOP) 框架.
背景情况:
- 准确和机械透明的模型对于内分泌危害评估至关重要.
- 现有的方法往往缺乏透明度或需要进行广泛的体内试验.
- 将分子数据与途径信息集成可以提高预测准确度.
研究的目的:
- 开发一种多模式,交叉注意力图框架,用于预测生物体水平内分泌干扰结果.
- 为了将分子图形与负面结果路径 (AOP) 固定的测试信号合并.
- 提高内分泌危害评估的机制透明度.
主要方法:
- 在Tier-1中利用多任务图形神经网络 (GNN) 来学习46个体外ToxCast/Tox21试验中的关键事件.
- 在Tier-2中使用交叉注意的多式联网GNN来整合路径信号与分子图.
- 应用双向交叉注意力和反事实性扰动来实现可解释性.
主要成果:
- 在体内赫什伯格 (AUROC = 0.97 ± 0.014) 和子宫变 (AUROC = 0.97 ± 0.008) 试验中获得了高预测性能.
- 对测试化合物的文献数据显示88%的一致性.
- 确定了关键的分子亚结构和影响预测的测试.
结论:
- 开发的框架准确地预测了内分泌干扰的结果,并具有机械解释性.
- 这种方法支持化学安全评估的综合方法中的有针对性的测试策略.
- 该模型提高了内分泌危害评估的透明度和准确性,与监管需求保持一致.
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