Interpreting Mass Spectrometry Data From Computational Chemistry: A Practical Guide to Reactivity and the Dissociation Mechanisms of 1,4‐Naphthoquinone Derivatives

ABSTRACT

Computational chemistry has played a key role in helping scientists understand ionization and molecular dissociation in the gas phase, which has contributed to its growing popularity among users of various mass spectrometry techniques. Early studies laid a strong foundation for applying computational methods to explore the stability and reactivity of gas‐phase ions, making these approaches more accessible and widely used across different research areas. In this work, we present a practical guide for effectively using computational chemistry in mass spectrometry studies. By comparing experimental results obtained in ESI‐MS/MS with those from quantum chemical models (DFT and composite methods) and spectra simulations (QCxMS and CFM‐ID), we aim to provide researchers with clear steps for studying organic molecules from ESI‐MS and MS/MS data and understanding the protonation in mass spectrometry. Proton affinity and gas‐phase basicities were calculated and the protonation sites were proposed. These results were compared to atomic charges, molecular orbitals, and Quantum Theory of Atoms‐in‐Molecules (QTAIM) calculations. Fragmentation mechanisms were proposed from protonated species, and the MS/MS spectra were compared to those predicted using CFM‐ID and QCxMS methods. The 2‐methyl‐1,4‐naphthoquinone analysis was used as a reference molecule for computational studies and benchmark. It is suggested the useful toolkits to describe molecular ionization/dissociation in ESI‐MS and ESI‐MS/MS studies from quantum chemical calculations by using a guide for the systematic application of computational chemistry in mass spectrometry and fragmentation mechanisms studies. Finally, an application was demonstrated for 2‐hydroxy‐3‐benzamine‐1,4‐naphthoquinone ionized in positive mode, highlighting the versatility of this guideline for studies involving more structural complexity.