Accuracy and feasibility analysis of computational chemistry in drug spectral simulation-a case study of acetylsalicylic acid.

Background: Traditional pharmaceutical experiments often involve diverse drug compounds with distinct synthesis and identification requirements, typically relying on substantial amounts of chemical reagents and sophisticated analytical instruments-factors that present notable limitations in teaching environments.

Objectives: This study aims to demonstrate the feasibility and educational value of integrating computational methods into drug synthesis and analysis, using acetylsalicylic acid (ASA) as a case study.

Methods: Students synthesized ASA experimentally and used molecular modeling and spectral simulation tools to analyze the compound. Computational techniques were employed to simulate UV-Vis, infrared (IR), and Raman spectra, with comparisons made to experimental results. The COSMO solvation model was applied to investigate solvent effects, and discrepancies in spectral peak assignments were resolved using computational data.

Results: Comparison of experimental and simulated spectra demonstrated high consistency, with R² values of 0.9933 and 0.9995, confirming the predictive power of the computational model. Solvent effects, such as the redshift of UV absorption in aqueous media, were successfully reproduced. Computational analysis resolved ambiguous IR peak assignments caused by overlap or impurities. While limitations such as the lack of NMR data, use of a single functional (GGA/BLYP), and simplified solvation were acknowledged, the integrated approach significantly improved student engagement and conceptual understanding. This study demonstrates the pedagogical and analytical benefits of combining experimental and computational methods, enhancing interpretative accuracy, supporting green chemistry, and offering a reproducible, resource-efficient framework for pharmaceutical education.