AI-Assisted Spectroscopy and Molecular Structure Elucidation: Recent Progress, Challenges, and Future Research Directions

Abstract

Artificial intelligence is increasingly transforming spectroscopy and molecular structure elucidation by enabling automated spectral processing, pattern recognition, molecular property prediction, candidate ranking, and data-driven interpretation across complex analytical platforms. This review examines recent progress in AI-assisted nuclear magnetic resonance, mass spectrometry, infrared, Raman, ultraviolet-visible, fluorescence, and emerging spectroscopies, together with advances in multimodal data fusion, deep learning, generative modelling, and automated structure generation. Current evidence indicates that machine-learning and deep-learning approaches can improve peak detection, chemical shift prediction, fragmentation analysis, molecular fingerprint classification, spectral simulation, and inverse structure elucidation while reducing interpretive workload. Multimodal models further strengthen structural confidence by integrating complementary spectral and molecular information. Important barriers remain, including small and chemically imbalanced datasets, inconsistent preprocessing, instrument-dependent variability, limited external validation, weak interpretability, uncertain confidence calibration, and the generation of chemically implausible outputs. Open and FAIR spectral repositories, standardized acquisition protocols, chemically informed data augmentation, explainable and uncertainty-aware models, and physics-constrained learning are required to improve reproducibility and generalizability. Prospective validation within real laboratory workflows and closer integration with autonomous experimental systems will be essential. AI should therefore function as an advanced analytical partner that enhances expert reasoning, accelerates molecular characterization, and supports reliable applications in pharmaceutical development, materials science, chemical discovery, clinical diagnostics, and environmental analytical research.