MACHINE LEARNING APPROACHES FOR ANALYZING C-13 NMR DATA IN ORGANIC MOLECULES (The Research of Medical Science Review, 2025)
This research introduces a sophisticated artificial intelligence (AI)-driven expert system tailored to support and streamline the interpretation of carbon-13 nuclear magnetic resonance (¹³C NMR) spectra, particularly in the structural analysis of complex organic compounds. The proposed system is anchored by a dynamically evolving knowledge base comprising machine-generated rules, which are systematically derived from extensive datasets of known chemical structures. These rules establish direct correlations between distinct spectral features and specific molecular substructures, thereby enhancing both the interpretative precision and predictive capabilities of the system.By integrating these AI-derived inference rules, the expert system not only improves the reliability of spectral prediction but also provides a robust framework for elucidating the structure of previously unidentified organic molecules. At the heart of the system lies a constraint-refinement search algorithm, designed to methodically narrow down structural possibilities through iterative rule-based filtering. This algorithmic approach significantly outperforms traditional analytical techniques by delivering more accurate, efficient, and scalable interpretations of ¹³C NMR data.The study underscores the transformative potential of artificial intelligence in computational organic chemistry, highlighting its ability to automate complex analytical workflows and drive advancements in molecular spectroscopy. Ultimately, this work sets a foundation for future developments in intelligent chemical analysis tools, bridging the gap between computational modeling and practical spectroscopy applications