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设计布赫瓦尔德-哈特维格反应图用于收益预测
Weiren Zhao1, Shen Wang1,2, Yang Li1
1State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Journal of organic chemistry
|September 10, 2025
概括
一个新的Buchwald-Hartwig (B-H) 反应图和深度学习模型 (BH-RGNN) 准确地预测了反应产量. 该模型确定了优化B-H反应的关键基本特征.
科学领域:
- 计算化学的计算化学
- 机器学习在化学中的应用
- 有机合成 有机合成
背景情况:
- 布赫瓦尔德-哈特维格 (B-H) 反应是有机合成中的一个关键的交叉合反应.
- 预测反应结果和优化条件仍然具有挑战性.
- 当前的计算方法可能无法完全捕捉复杂的反应物相互作用.
研究的目的:
- 为深度学习开发一种新的图形表示,以模拟B-H反应.
- 创建一个图形神经网络 (GNN) 模型来预测B-H反应产量.
- 确定影响反应产量的关键因素,特别是基的作用.
主要方法:
- 设计了一个定制的反应图,将反应物表示为节点.
- 开发了一个布赫瓦尔德-哈特维格反应图神经网络 (BH-RGNN) 模型.
- 在高通量B-H反应数据集上训练模型.
- 采用基于扰动的分析来理解模型预测.
主要成果:
- 在BH-RGNN实现了高预测性能,R2得分为0.971.971.
- 该模型显示了较低的计算成本.
- 扰动分析揭示了有关基配体相互作用及其对反应产量的影响的重要见解.
- 确定了对于优化反应结果至关重要的特定基因特性.
结论:
- 量身定制的图形表示对于化学反应建模中的 GNN 有效.
- BH-RGNN为预测反应产量和指导实验设计提供了一个强大的工具.
- 这种方法为B-H反应中的基选择提供了有价值的指导,提高了合成效率.
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