挑战反应预测模型,将其泛化为新化学
John Bradshaw1, Anji Zhang2, Babak Mahjour1
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
ACS central science
|April 28, 2025
概括
用于预测有机反应的深度学习模型与真实世界的数据作斗争. 新的评估表明,当前的模型在分布之外的任务中表现不佳,这凸显了在AI反应预测中需要更好的推断能力.
科学领域:
- 有机化学 有机化学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习模型越来越多地用于预测有机反应产物,帮助逆合成和分子设计.
- 现有的基准通常评估分布式模型,这些模型可能不反映现实世界的表现.
- 当前的模型可以产生错误的预测,当应用于新或外的分布场景.
研究的目的:
- 评估原型基于SMILES的深度学习反应预测器的分布外概括能力.
- 在现实的部署设置中确定当前反应预测模型的局限性.
- 建立一个框架来评估超越标准基准的模型性能.
主要方法:
- 对基于SMILES的深度学习模型进行了评估,对具有挑战性的分发外任务进行了评估.
- 对来自新专利和作者的数据进行绩效评估,与培训数据不同.
- 通过使用训练集几年后发表的反应进行时间分割评估.
- 在不同反应类别中检查模型外推.
主要成果:
- 随机采样数据集的性能比对新专利或作者的概括过于乐观.
- 模型在与训练数据相比显著晚些时候发布的反应上进行测试时显示性能下降.
- 跨反应类的推断揭示了预测新型反应类型的局限性.
结论:
- 目前用于反应预测的深度学习模型在分布之外的环境中表现出显著的局限性.
- 标准基准没有充分捕捉现实应用和推断的挑战.
- 需要进一步开发以创建能够进行强大的反应发现和预测的下一代模型.
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