合成适用性领域 (SynAD):导航化学空间可靠的人工智能驱动的反应预测.
Zhenzhi Tan1, Qi Yang1,2, Long Zhang1,3
1Center of Basic Molecular Science, Department of Chemistry, Tsinghua University, Beijing, 100084, P.R. China.
Angewandte Chemie (International ed. in English)
|January 22, 2026
概括
本研究介绍了SynAD,这是一种机器学习框架,用于定义有机合成中人工智能 (AI) 模型的可靠化学空间. SynAD帮助化学家们相信AI的预测,并专注于实验,加速新发现.
科学领域:
- 有机合成化学 有机合成化学
- 人工智能的人工智能是人工智能.
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 正在通过加速假设评估和减少实验努力来改变有机合成.
- 一个关键的局限性是分布外 (OOD) 问题,人工智能模型无法预测使用新型催化剂,基质或条件的未见反应.
- 准确预测反应结果对于有效的药物发现和材料科学至关重要.
研究的目的:
- 引入SynAD,这是一个机器学习框架,用于评估有机合成中AI模型的预测能力.
- 在AI模型所覆盖的化学空间内自动划分可靠和不可靠的反应.
- 为化学家提供一种工具,以信任人工智能预测,并战略性地分配资源进行新的发现.
主要方法:
- SynAD将化学描述符与适应模型的距离指标相结合,以定义合成适用性领域.
- 该框架在Ullmann Ligand数据集 (ULD) 和六个额外的反应数据集上得到了验证.
- 开发了一个SynAD评分来量化反应类的可预测性.
主要成果:
- SynAD成功地区分了可预测的化学空间,在ULD上实现了R2 = 0.90 (覆盖率为12.3%) 的预测准确度,这与R2 = -0.21.21的基线相比是显著的改善.
- 该框架在多个数据集中展示了一致的性能,证实了其可通用性.
- SynAD评分有效量化反应类的可预测性,指导实验重点.
结论:
- SynAD建立了一个关键框架,用于定义有机合成中AI预测能力的极限.
- 通过识别可靠的反应空间,SynAD使化学家能够信任AI工具并优化实验资源分配.
- 这种方法通过为人工智能驱动的研究提供必要的护来加速新分子发现的步伐.
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