ToxACoL:一种终点意识和以任务为中心的化合物表示学习范式,用于急性毒性评估
Jiang Lu1,2,3, Lianlian Wu1,2, Ruijiang Li2
1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.
一种新的机器学习方法,辅助相关性学习 (ToxACoL),改善了多条件急性毒性预测,特别是对于数据稀缺的人类终点. 这种方法通过减少数据需求和提高准确性来增强化学品安全评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 多种急性毒性评估对于化学品安全,分类和风险管理至关重要.
- 当前的深度学习模型面临着各种实验条件,不平衡的数据集和有限的目标数据的挑战,这会影响数据稀缺的终点的预测准确性.
- 准确预测化学毒性对于符合法规和保护人类健康至关重要.
研究的目的:
- 开发一种新的机器学习范式,即辅助相关性学习 (ToxACoL),用于可靠的多条件急性毒性评估.
- 解决现有方法在处理各种实验条件和稀缺数据方面的局限性.
- 改进对数据稀缺的毒理学终点的预测,并促进跨条件的知识转移.
主要方法:
- 辅助关联学习 (ToxACoL) 模型使用图形拓和通过图形卷积转移知识的终点关联.
- 附加的关联机制同步编码化合物和终点,生成终点意识和以任务为中心的表示.
- 评估了ToxACoL在多条件急性毒性预测方面的表现,包括处理数据稀缺的能力.
主要成果:
- ToxACoL表现出显著的改善,在数据稀缺的人类终点方面实现了43%-87%的增强.
- 该模型实现了培训数据需求的大幅减少,需要70%至80%的数据少.
- 学习表征的可视化提供了对结构性警报机制的见解,以及动物对人类毒性的潜在推断.
结论:
- ToxACoL为多条件急性毒性评估提供了一种强大且数据效率高的解决方案.
- 开发的平台可以帮助预测化学毒性,可能弥合动物和人类数据之间的差距.
- 这种方法提高了化学安全评估的准确性和可靠性,特别是对于有限数据的终点.
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