An Artificial Intelligence Approach for Tackling Conformational Energy Uncertainties in Chiroptical Spectroscopies

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Determining the absolute configuration of chiral molecules is crucial for understanding chirality-dependent phenomena.
• Spectroscopic methods using polarized light are powerful but hindered by uncertainties in conformational analysis.
• Accurate conformational Boltzmann factors are essential for reliable spectral predictions.

Purpose of the Study:

• To develop a novel computational approach for overcoming uncertainties in conformational Boltzmann factors for absolute configuration determination.
• To enhance the reliability of spectral predictions in chiroptical techniques.
• To address the challenge of assigning absolute configurations in conformationally complex chiral molecules.

Main Methods:

• Combines a genetic algorithm to identify relevant conformers, accounting for DFT energy uncertainties.
• Employs a hierarchical clustering algorithm to analyze spectral trends and assess prediction reliability.
• Applies the method to challenging bis-indane natural products, papuamine and haliclonadiamine.

Main Results:

• The novel approach successfully tackles uncertainties in conformational Boltzmann factors.
• The method effectively identifies relevant conformers and analyzes spectral trends.
• Demonstrated effectiveness on complex natural products with multiple chiral centers and conformational heterogeneity.

Conclusions:

• The developed computational strategy provides a robust solution for absolute configuration determination.
• This method enhances the predictive power of chiroptical techniques for complex chiral molecules.
• Offers a reliable approach for cases previously unassignable by existing methods.