Predictions of Steady-State Photo-CIDNP Enhancement by Machine Learning.

ABSTRACT: 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. Furth

SUBMITTER: Stefanska M 

PROVIDER: S-EPMC12333361 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature