Computer Assisted Structure Elucidation (CASE): Current and future perspectives.

Area of Science:

• Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Computer-Assisted Structure Elucidation (CASE) methods, primarily using Nuclear Magnetic Resonance (NMR) data, have been developed over 50 years.
• CASE expert systems have matured, successfully elucidating and revising numerous complex natural product structures.

Purpose of the Study:

• To discuss the future evolution of Computer-Assisted Structure Elucidation (CASE).
• To highlight the synergistic interactions between CASE, advanced NMR techniques, and computational chemistry.

Main Methods:

• Review of current CASE systems and their reliance on 1D and 2D NMR data.
• Discussion of emerging NMR experiments (e.g., long-range correlation, pure-shift, RDCs, RCSAs).
• Integration of computational chemistry (DFT) and machine learning algorithms with CASE.

Main Results:

• CASE systems are expected to evolve through synergistic interactions with new NMR experiments and computational chemistry.
• Advancements in NMR, DFT, and machine learning will influence CASE development.
• New analytical techniques like AFM, crystalline sponge X-ray analysis, and micro-ED will complement CASE.

Conclusions:

• CASE is predicted to become a widely utilized routine tool in academic and industrial laboratories.
• The "golden age" of CASE is anticipated to be in the future, driven by ongoing technological advancements.