Advances and perspectives in computer-assisted structure elucidation: a review

Abstract

Computer-Assisted Structure Elucidation (CASE) is a powerful yet underused approach in chemistry to determine molecular structures from experimental data without necessarily being restricted to the contents of chemical databases. This review provides a comprehensive overview of the current state of CASE, encompassing methodologies, computational techniques, applications, challenges, and future directions. The historical evolution of CASE tools is traced, highlighting key milestones and influential technologies. Moreover, the methodologies employed in CASE, including reduction and assembly methods, as well as hybrid approaches, are examined. Special attention is given to the integration of analytical data, such as NMR, MS, and IR, into CASE algorithms, along with computational techniques such as machine learning approaches. Through a series of case studies and real-world applications, the utility of CASE tools in drug discovery, natural products chemistry, environmental sciences, and metabolomics is illustrated. Despite advancements, challenges persist in handling complex molecular structures, improving algorithm accuracy, integrating heterogeneous data sources, benchmarking and reconciling diverse programming languages, alongside the mixture of open vs. closed source developments. Looking ahead, emerging trends and future directions in CASE are identified, including rapid developments with the adoption of deep learning and big data analytics. By providing insights into the current landscape of CASE, highlighting the challenges and proposing recommendations for future research, this review aims to stimulate further CASE innovation and collaboration.