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Photochemical Electrocyclic Reactions: Stereochemistry01:26

Photochemical Electrocyclic Reactions: Stereochemistry

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The absorption of UV–visible light by conjugated systems causes the promotion of an electron from the ground state to the excited state. Consequently, photochemical electrocyclic reactions proceed via the excited-state HOMO rather than the ground-state HOMO. Since the ground- and excited-state HOMOs have different symmetries, the stereochemical outcome of electrocyclic reactions depends on the mode of activation; i.e., thermal or photochemical.
Selection Rules: Photochemical Activation
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Updated: Sep 8, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
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Predictions of Steady-State Photo-CIDNP Enhancement by Machine Learning.

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Machine learning accurately predicts photochemically induced dynamic nuclear polarization (photo-CIDNP) signal enhancement. This approach enables virtual prescreening of molecules, reducing the need for time-consuming experimental screening in NMR spectroscopy.

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Area of Science:

  • Nuclear Magnetic Resonance (NMR) spectroscopy
  • Hyperpolarization techniques
  • Computational chemistry

Background:

  • Photochemically induced dynamic nuclear polarization (photo-CIDNP) enhances NMR signal sensitivity.
  • Current limitations exist in predicting steady-state photo-CIDNP enhancement, necessitating extensive experimental screening.
  • Developing predictive models is crucial for efficient identification of suitable target molecules.

Purpose of the Study:

  • To explore the application of machine learning for predicting steady-state photo-CIDNP signal-to-noise enhancement (SNE).
  • To identify key molecular features that correlate with photo-CIDNP enhancement.
  • To enable virtual prescreening of compound libraries for photo-CIDNP applications.

Main Methods:

  • Measurement of steady-state photo-CIDNP SNE for 40 derivatives (indole, amino acid, phenol).
  • Correlation of experimental SNE with eight molecular features.
  • Development and evaluation of machine learning models (Logistic Regression, CatBoost Regressor, K-Nearest Neighbors).

Main Results:

  • The nucleophilic Fukui index was identified as a key qualitative predictor of maximal SNE.
  • A Logistic Regression model achieved 100% accuracy in identifying sites with high enhancement (SNE > 90).
  • CatBoost Regressor and K-Nearest Neighbors demonstrated superior performance in quantitative SNE prediction.

Conclusions:

  • Machine learning models show significant potential for predicting photo-CIDNP SNE.
  • This approach can streamline the identification of molecules for hyperpolarization experiments.
  • Virtual prescreening using ML can accelerate research in NMR spectroscopy.