用数据驱动的预测对无非对称的二化:模型开发和实验验证
Blake E Ocampo1, Bilal Altundas1, Matthew J Bock1
1Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States.
ACS central science
|September 29, 2025
概括
化学信息学通过预测酶选择性来增强Sharpless不对称二化 (AD) 反应. 机器学习模型准确预测选择性,改善化学合成结果.
科学领域:
- 有机化学 有机化学
- 计算化学计算化学
- 化学信息学是一种化学信息学.
背景情况:
- 沙普莱斯的非对称二化在化学合成中至关重要,但可以表现出可变的选择性.
- 了解和预测选择性对于优化反应结果至关重要.
研究的目的:
- 开发一个数据驱动的化学信息工作流程,用于分析夏普莱斯非对称二化.
- 构建机器学习模型来预测这种反应中的酶选择性.
主要方法:
- 策划了1007个反应的数据库,使用文献中的AD混合α和β.
- 实现了对齐依赖的,基于碎片的基特征化,用于建模.
- 利用机器学习和夏普利添加式解释 (SHAP) 进行分析.
主要成果:
- 在Q2F3≥0.8和试验r2≥0.7.7的情况下实现了高预测性能.
- 模型显示平均绝对误差 (MAE) ≤0.3千卡/毫升.
- SHAP分析确定了影响选择性预测的关键基特征.
结论:
- 开发的化学信息平台准确地预测了夏普莱斯非对称二化酶的酶选择性.
- 模型显示了对新型,样本外基的可靠性能.
- 这种方法为控制反应选择性的因素提供了有价值的见解.
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