深度学习增强的QSAR建模用于预测发育性神经毒性,基于来自不良结果途径的分子启动事件
Eufrásia de Sousa Pereira1, Vinícius Alexandre Fiaia Costa1, Eder Soares de Almeida Santos1
1Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil.
Molecular diversity
|January 23, 2026
概括
这项研究引入了一个深度学习模型,从化学结构中预测发育神经毒性 (DNT),减少动物测试. 该框架准确地确定了农药污染大麻暴露的毒性途径中的关键事件.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 毒理学中的深度学习.
背景情况:
- 发育神经毒性 (DNT) 存在化学物质暴露的风险.
- 传统的毒性测试是缓慢的,昂贵的,并使用许多动物.
- 为了高效的化学安全评估,需要预测模型.
研究的目的:
- 开发一个深度学习-定量结构-活动关系 (QSAR) 框架.
- 在不良结果途径 (AOP) 中预测分子启动事件 (MIEs) 和关键事件 (KEs) 的结合亲缘关系.
- 评估受农药污染的大麻DNT风险.
主要方法:
- 在来自ChEMBL数据库的24,476个化合物上训练深度神经网络 (DNN).
- 在4个MIE和6个KE任务上测试模型.
- 用于模型解释性和ECFP4特征映射的SHAP值.
主要成果:
- 实现了高预测性能,平均相关系数为0.82 ± 0.05.05.
- 在预测DNT和识别相关MIEs和KEEs方面表现出很强的准确性.
- 可视化特征的贡献,以了解毒性机制.
结论:
- 开发的深度学习框架准确地预测DNT.
- 该模型有效地识别了神经毒性途径中的关键MIE和KE.
- 这种方法为传统的DNT测试提供了更快,更具成本效益的替代方案.
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