Towards analytical standards for new psychoactive substances: The contribution of computational chemistry and statistical methods

New psychoactive substances (NPS) present significant challenges for law enforcement and public health due to their rapid emergence and structural diversity, often outpacing the development of traditional analytical methods. This review explores using computational chemistry, particularly density functional theory (DFT), to obtain infrared spectra. This combination to characterize NPS began in the 2010s and has gained momentum across all continents in recent years. We demonstrate that DFT can accurately predict the spectroscopic properties of NPS. In addition, statistical methods for comparing computational and experimental data are presented. A SWOT (Strengths, Weaknesses, Opportunities, Threats) assessment is performed to assess the potential and limitations of these computational approaches. Finally, integrating artificial intelligence and developing extensive spectral databases emerge as promising avenues for future research, provided that data reliability and rigorous validation processes are maintained. All these efforts are aligned to obtain faster responses and keep up with NPS advances.