送神经网络用于预测非对称Negishi反应的酶选择性
Abbigayle E Cuomo1, Sebastian Ibarraran1, Sanil Sreekumar2
1Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States.
ACS central science
|October 2, 2023
概括
这项研究使用密度功能理论 (DFT) 过渡状态 (TS) 数据训练神经网络,以预测化学合成中的enantioselectivity,即使使用小数据集.
科学领域:
- 计算化学计算化学
- 有机合成 有机合成
- 机器学习 机器学习
背景情况:
- 密度函数理论 (DFT) 对于预测化学反应选择性至关重要.
- 预测复杂合成中的选择性是具有挑战性的,因为能量差异很小.
- 数据科学提供了互补的方法,但与有限的有机合成数据集作斗争.
研究的目的:
- 开发一种机器学习模型,用于预测Negishi交叉合反应中的酶选择性.
- 调查DFT衍生特征对于在小数据集上训练神经网络的实用性.
- 为了将这种DFT增强的机器学习方法与传统方法进行比较.
主要方法:
- 使用DFT计算来获得过渡状态 (TS) 能量.
- 来自DFT TS计算的工程特征来标记一个小数据集.
- 训练了一个前神经网络来预测enantioselectivity.
- 对神经网络中DFT能量和连接体/基态特征进行性能比较.
主要成果:
- 在DFT TS上训练的前神经网络特征成功预测了enantioselectivity.
- 这种方法即使在有限的数据集上也被证明是有效的.
- 与传统方法相比,DFT增强模型显示出具有竞争力或优异的性能.
结论:
- 将DFT计算与机器学习相结合,为预测有机合成中的enantioselectivity提供了一个可行的策略.
- 这种方法克服了计算化学中小型数据集的局限性.
- 该方法为设计选择性化学转换提供了一个强大的工具.
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