艾姆斯前:使用图形变压器进行最先进的突变性预测
Luke A Thompson1, Josiah G Evans1, Slade T Matthews1
1Sydney Pharmacy School, The University of Sydney, Sydney 2006, Australia.
Chemical research in toxicology
|June 26, 2025
概括
新的图形变压器模型AmesFormer在使用Ames测试预测化学变异性方面取得了最先进的性能. 这种可访问的开源工具增强了药物开发的化学安全评估.
科学领域:
- 计算毒理学计算毒理学
- 机器学习在药物发现中的作用
背景情况:
- 艾姆斯突变性测试对于化学安全评估至关重要.
- 目前的in silico模型经常使用复杂的组合和分子指纹,忽视整体分子结构.
研究的目的:
- 介绍AmesFormer,一个新的图形转换器神经网络用于Ames变异性预测.
- 为了展示AmesFormer的最新性能和可访问性,用于监管和药物开发应用.
主要方法:
- 开发AmesFormer,一个图形转换器神经网络.
- 创建一个新的Ames数据集,用于模型培训和验证.
- 在标准化数据集上对比AmesFormer与其他22个Ames模型.
- 使用温度缩放对模型校准性能进行评估.
主要成果:
- 在Ames的突变性预测中,AmesFormer取得了最先进的 (SOTA) 性能.
- 该模型在与新的Ames数据集配对时表现出高性能.
- 校准性能通过使用温度缩放进行评估和改进.
结论:
- 艾姆斯Former提供了一个高性能,可访问和开源的计算模型,用于艾姆斯变异性.
- 该模型具有显著的潜力,可以帮助监管决策和加速药物开发过程.
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