ReCQC: A Novel NMR Data Analysis Platform Leveraging ¹³ C NMR and HSQC Database Strategies for Natural Product Dereplication

Leveraging 13 C NMR spectroscopic data, in this study, an innovative methodology is proposed for the efficient and accurate elucidation of the skeletal structures of target components within complex mixtures through the identification of maximum substructures and implementation of dynamic scoring. This approach not only facilitates targeted compound isolation but also enables iterative optimization of scoring outcomes. Using this approach, the team developed ReCQC 1.0, a publicly accessible software platform for mixture dereplication. To further increase analytical precision, advanced research was conducted to refine the scoring algorithm through the incorporation of carbon chemical environment descriptors integrated with Euclidean distance-based metrics derived from HSQC correlations, culminating in the development of ReCQC 2.0. Both software iterations feature intuitive graphical user interfaces designed for operational efficiency and accessibility. Comprehensive recovery experiments demonstrate the superior performance of the ReCQC framework compared with conventional dereplication strategies. Validation through chemical profiling of Pueraria lobata and Nicotiana tabacum enabled the identification of 25 constituent compounds, establishing a robust analytical pipeline for natural product mixture characterization and providing a valuable reference for future research endeavors.