人工智能和机器学习方法评估心脏毒性后的不良结果路径框架
Edoardo Luca Viganò1, Davide Ballabio2, Alessandra Roncaglioni1
1Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milan, Italy.
Toxics
|January 26, 2024
概括
人工智能和机器学习通过分析不良结果途径 (AOP) 内的相互作用来预测化学心脏毒性. 这种方法减少了动物试验,并加强了化学品安全评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 心血管疾病是全球主要的健康问题.
- 环境污染物,农药和药物等化学物质可以通过各种不良结果途径 (AOP) 引起心脏毒性.
- 化学品之间的协同作用使危险评估复杂化.
研究的目的:
- 使用人工智能 (AI) 和机器学习 (ML) 来评估化学心脏毒性.
- 在AOP网络中分析与生物点的化学相互作用.
- 为AI/ML模型开发和评估编码化学信息的先进方法.
主要方法:
- 利用ML和AI来建模心脏毒性AOP中的化学相互作用,从分子发起事件 (MIE) 到关键事件 (KE).
- 探索各种化学编码策略,包括分子描述符,指纹,基于图形的方法,自动编码器和字符嵌入.
- 开发了一种多模式神经网络架构,以整合各种化学表征.
主要成果:
- 证明了AI/ML在预测化学心脏毒性的有效性.
- 展示了先进的化学编码方法,以提高模型性能.
- 强调了多式联网架构的优势,特别是随着数据量增加.
结论:
- 人工智能和机器学习为心脏毒性评估提供了强大的in silico工具,补充了传统方法.
- 先进的化学表示和多式联网提高了危险评估的准确性和效率.
- 这种方法支持综合测试和评估方法 (IATA),减少对体内研究的依赖.
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