期刊
CHEMICAL SCIENCE
卷 12, 期 20, 页码 6879-6889出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1sc00482d
关键词
-
资金
- EPFL
- European Research Council (ERC) [817977]
- Swiss National Science Foundation (SNSF)
- NCCR Materials' Revolution: Computational Design and Discovery of Novel Materials (MARVEL)
- European Research Council (ERC) [817977] Funding Source: European Research Council (ERC)
Hundreds of catalytic methods are developed annually to meet the demand for high-purity chiral compounds, but designing enantioselective organocatalysts remains a challenge. Recent research suggests that combining quantum chemical computations and machine learning can lead to advancements in asymmetric catalysis.
Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol(-1) were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据