  In recent years, conventional chemistry techniques have faced significant
challenges due to their inherent limitations, struggling to cope with the
increasing complexity and volume of data generated in contemporary research
endeavors. Computational methodologies represent robust tools in the field of
chemistry, offering the capacity to harness potent machine-learning models to
yield insightful analytical outcomes. This review delves into the spectrum of
computational strategies available for natural product analysis and constructs
a research framework for investigating both qualitative and quantitative
chemistry problems. Our objective is to present a novel perspective on the
symbiosis of machine learning and chemistry, with the potential to catalyze a
transformation in the field of natural product analysis.
