A systematic approach for the generation and verification of structural hypotheses

Area of Science:

• Computational Chemistry
• Organic Chemistry
• Spectroscopy

Background:

• Molecular structure elucidation is crucial in chemistry.
• Chemical shift prediction aids in selecting structural hypotheses.
• Current methods rely on empirical or quantum-mechanical (QM) approaches, often using GIAO/DFT for NMR prediction.

Purpose of the Study:

• To demonstrate that expert systems offer a more rational approach to generating structural hypotheses.
• To highlight the utility of expert systems in deducing structures from experimental spectral data, particularly 2D NMR.
• To propose an optimized workflow for molecular structure elucidation.

Main Methods:

• Application of an expert system to deduce potential structures from experimental spectral data (2D NMR).
• Utilizing chemical shift prediction algorithms (incremental or neural net) to rank deduced structures.
• Employing time-consuming QM calculations for final confirmation of the most probable structure(s).

Main Results:

• Expert systems can systematically generate all potential structures consistent with spectral data.
• Chemical shift prediction using incremental or neural net algorithms effectively distinguishes the best structural candidates.
• This integrated approach streamlines the structure elucidation process.

Conclusions:

• Expert systems provide a superior method for generating structural hypotheses compared to traditional approaches.
• Combining expert systems with efficient chemical shift prediction optimizes molecular structure elucidation.
• QM calculations serve as a valuable, targeted confirmation tool for high-probability structures.