Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Combined Experimental and Theoretical Insights: Spectroscopic and Molecular Investigation of Polyphenols from Fagonia indica via DFT, UV-vis, and FT-IR Approaches

This review deals with computational study of polyphenolic compounds of medicinal importance and interest for drug development. Herein, four polyphenolic compounds comprising catechol (1), caffeic acid (II), gallic acid (III), and pyrogallol (IV) have been isolated from a medicinal specie, Fagonia indica, by applying silica gel column chromatography. These compounds were identified by using gas chromatography-mass spectrometry (GC-MS) analysis and confirmed by geometric computational analysis. According to computational results, caffeic acid has shown the highest biological activation due to higher chemical softness, electronegativity (χ (eV) = -648.644), and electrostatic potential value (-8.424 × 10-2 to +8.424 × 10-2), while smaller values of chemical potential (-0.269), ELUMO (-0.080), and energy gap (ΔE = 0.149). The Mulliken atomic charges were calculated by using DFT/B3LYP with basis set 6-311G for the determination of active sites. The oxygen atom of catechol showed highest nucleophilic characteristic with a more negative charge (08 = -0.695), and pyrogallol indicated a strong electrophilic center at C14 = 0.415 with a higher positive charge. Moreover, UV-visible absorption spectra and a detailed study of vibrational frequencies for all phenolic compounds by employing the DFT approach with 3-21G, 6-31G, and 6-311G basis sets at the ground-state level showed the great agreement with experimental results. ANOVA has been applied to validate the theoretical data. Results suggest that compounds I-IV are suitable in diverse fields.