Evaluation of Density Functional Theory-Generated Data for Infrared Spectroscopy of Novel Psychoactive Substances Using Unsupervised Learning

Novel psychoactive substances (NPSs) are compounds plotted to modify the chemical structures of prohibited substances, offering alternatives for consumption and evading legislation. The prompt emergence of these substances presents challenges in health concerns and forensic assessment because of the lack of analytical standards. A viable alternative for establishing these standards involves leveraging in silico methods to acquire spectroscopic data. This study assesses the efficacy of utilizing infrared spectroscopy (IRS) data derived from density functional theory (DFT) for analyzing NPSs. Various functionals were employed to generate infrared spectra for five distinct NPS categories including the following: amphetamines, benzodiazepines, synthetic cannabinoids, cathinones, and fentanyls. PRISMA software was conceived to rationalize data management. Unsupervised learning techniques, including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE), were utilized to refine the assessment process. Our findings reveal no significant disparities among the different functionals used to generate infrared spectra data. Additionally, the application of unsupervised learning demonstrated adequate segregation of NPSs within their respective groups. In conclusion, integrating theoretical data and dimension reduction techniques proves to be a powerful strategy for evaluating the spectroscopic characteristics of NPSs. This underscores the potential of this combined methodology as a diagnostic tool for distinguishing IR spectra across various NPS groups, facilitating the evaluation of newly unknown compounds.