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Python-Based Mechanistic Evaluation of Antioxidant Activity Using DFT Calculations


Authors : Naman Gupta

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/mt83b3n7

Scribd : https://tinyurl.com/rfy5tmd4

DOI : https://doi.org/10.38124/ijisrt/26jul320

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Abstract : Cardiovascular disease, chronic inflammation, neurodegeneration, aging, and cancer are largely associated with oxidative stress due to the accumulation of reactive oxygen species (ROS). Hydrogen Atom Transfer (HAT) and Single Electron Transfer (SET) are the strategies adopted by antioxidants to neutralize ROS, both of which can be examined using quantum-chemical descriptors, including bond dissociation enthalpy (BDE), ionization potential (IP), and the energies of frontier molecular orbitals (FMOs). This study evaluates twelve phenolic and aromatic compounds, including phenol, catechol, hydroquinone, pyrogallol, caffeic acid, ferulic acid, gallic acid, and resveratrol, using Density Functional Theory (DFT) at the B3LYP/6-31G(d,p) level in Gaussian 09. Although DFT calculations have been employed to investigate antioxidants, most studies are limited to individual compounds and rely on manual extraction of computational descriptors. Automated workflows that help to integrate the extraction of descriptors, classification of mechanistic pathways, and comparative evaluation across a range of antioxidants remain unexplored. Geometry optimization and vibrational frequency analysis confirmed structural stability; BDEs and IPs, along with energy gaps between the FMOs, were useful for analyzing mechanistic antioxidant behavior. A Python-based automation workflow for extracting quantum-chemical descriptors from Gaussian output files was useful for calculating antioxidant parameters and generating structured datasets and visualizations. Molecules rich in hydroxyl groups (-OH) exhibited stronger HAT activity via radical stabilization and effective hydrogen donation, whereas conjugated systems such as resveratrol and ferulic acid preferred SET pathways due to lower IPs and effective electronic delocalization. The study demonstrates the integration of a computational framework connecting DFT-based mechanistic studies with automated extraction of descriptors to obtain antioxidant analysis that are reproducible.

Keywords : Antioxidants, Density Functional Theory (DFT), Hydrogen Atom Transfer (HAT), Single Electron Transfer (SET), Bond Dissociation Enthalpy (BDE), Ionization Potential (IP), Python Workflow.

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Cardiovascular disease, chronic inflammation, neurodegeneration, aging, and cancer are largely associated with oxidative stress due to the accumulation of reactive oxygen species (ROS). Hydrogen Atom Transfer (HAT) and Single Electron Transfer (SET) are the strategies adopted by antioxidants to neutralize ROS, both of which can be examined using quantum-chemical descriptors, including bond dissociation enthalpy (BDE), ionization potential (IP), and the energies of frontier molecular orbitals (FMOs). This study evaluates twelve phenolic and aromatic compounds, including phenol, catechol, hydroquinone, pyrogallol, caffeic acid, ferulic acid, gallic acid, and resveratrol, using Density Functional Theory (DFT) at the B3LYP/6-31G(d,p) level in Gaussian 09. Although DFT calculations have been employed to investigate antioxidants, most studies are limited to individual compounds and rely on manual extraction of computational descriptors. Automated workflows that help to integrate the extraction of descriptors, classification of mechanistic pathways, and comparative evaluation across a range of antioxidants remain unexplored. Geometry optimization and vibrational frequency analysis confirmed structural stability; BDEs and IPs, along with energy gaps between the FMOs, were useful for analyzing mechanistic antioxidant behavior. A Python-based automation workflow for extracting quantum-chemical descriptors from Gaussian output files was useful for calculating antioxidant parameters and generating structured datasets and visualizations. Molecules rich in hydroxyl groups (-OH) exhibited stronger HAT activity via radical stabilization and effective hydrogen donation, whereas conjugated systems such as resveratrol and ferulic acid preferred SET pathways due to lower IPs and effective electronic delocalization. The study demonstrates the integration of a computational framework connecting DFT-based mechanistic studies with automated extraction of descriptors to obtain antioxidant analysis that are reproducible.

Keywords : Antioxidants, Density Functional Theory (DFT), Hydrogen Atom Transfer (HAT), Single Electron Transfer (SET), Bond Dissociation Enthalpy (BDE), Ionization Potential (IP), Python Workflow.

Paper Submission Last Date
31 - July - 2026

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