Stefańska Marta, Müntener Thomas, Hiller Sebastian
Biozentrum, University of Basel, Spitalstrasse 41, Basel 4056, Switzerland.
J Am Chem Soc. 2025 Aug 6;147(31):27172-27178. doi: 10.1021/jacs.5c07462. Epub 2025 Jul 28.
Photochemically induced dynamic nuclear polarization (photo-CIDNP) is a hyperpolarization method used to boost signal sensitivity in NMR spectroscopy. So far, there is no theory to predict the steady-state photo-CIDNP enhancement reliably, and hence, suitable target molecules need to be identified through tedious experimental screenings. Here, we explore the use of machine learning to predict steady-state photo-CIDNP enhancement. For a series of 27 indole-, five amino-acid-, and eight phenol-derivatives, the signal-to-noise enhancement (SNE) of steady-state photo-CIDNP experiments was measured and then connected to a combination of eight molecular features. The nucleophilic Fukui index was identified as a strong qualitative indicator of the site with the highest SNE in each molecule. Furthermore, a semiquantitative machine learning model based on Logistic Regression identified the sites with high enhancements (SNE > 90) in 100% of cases. Among several quantitative machine learning models for enhancement prediction, CatBoost Regressor and K-Nearest Neighbors showed the best performance. The results demonstrate the high potential of machine learning approaches for predictions of photo-CIDNP SNE, which will enable virtual prescreening of compound libraries.
光化学诱导动态核极化（photo-CIDNP）是一种用于提高核磁共振波谱信号灵敏度的超极化方法。到目前为止，尚无理论能够可靠地预测稳态photo-CIDNP增强效果，因此，需要通过繁琐的实验筛选来确定合适的目标分子。在此，我们探索使用机器学习来预测稳态photo-CIDNP增强效果。对于一系列27种吲哚衍生物、5种氨基酸衍生物和8种苯酚衍生物，测量了稳态photo-CIDNP实验的信噪比增强（SNE），并将其与8种分子特征的组合相关联。亲核福井指数被确定为每个分子中SNE最高位点的强定性指标。此外，基于逻辑回归的半定量机器学习模型在100%的情况下识别出增强效果高（SNE>90）的位点。在几种用于增强预测的定量机器学习模型中，CatBoost回归器和K近邻算法表现最佳。结果表明，机器学习方法在预测photo-CIDNP SNE方面具有很高的潜力，这将能够对化合物库进行虚拟预筛选。
