Exploring the Physicochemical and Toxicological Study of G-Series and A-Series Agents Combining Molecular Dynamics and Quantitative Structure–Activity Relationship

This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve agents, both Ellison–Hoenig and Mirzayanov structural proposals were examined. MD simulations (10 ns, NPT ensemble) provided key thermodynamic properties, including density, molar heat capacity, and diffusivity. Simulated densities for G-agents (e.g., Sarin: 1.09 g/cm3, Soman: 1.03 g/cm3) and A-agents (e.g., A230: 1.608 g/cm3, Ellison–Hoenig model) closely matched experimental data. Heat capacities ranged from 258 to 462 J/mol·K, and self-diffusion coefficients revealed lower mobility for A-agents, especially under the Ellison–Hoenig configurations. QSAR modeling focused on lipophilicity (LogP) and acute toxicity (LD50). Predicted LD50 values ranged from 0.012 to 0.017 mg/kg for G-agents and up to 1.23 mg/kg for A-agents. A-234 showed the highest lipophilicity (LogP = 2.97) and toxicity (LD50 = 0.51 mg/kg) within its group. Additional descriptors, such as molecular weight and polar surface area, supported toxicity predictions. Strong correlations emerged between MD-derived properties and QSAR outputs, validating the integrated approach. The combined use of MD and QSAR techniques provided a comprehensive view of the agents’ environmental behavior and toxicological impact, supporting safer assessment strategies and reinforcing the importance of multidisciplinary modeling for chemical threat mitigation.

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Publication Details

Journal
ChemEngineering
Published
2025-08-18
DOI
https://doi.org/10.3390/chemengineering9040091
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
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article

Exploring the Physicochemical and Toxicological Study of G-Series and A-Series Agents Combining Molecular Dynamics and Quantitative Structure–Activity Relationship

Georgios Nikolaou, Antonios Koufou, Sotiria Anastasiou, Peter Roupas et al.
1 citations
ChemEngineering
Computational Drug Discovery Methods
1.50
article

Exploring the Physicochemical and Toxicological Study of G-Series and A-Series Agents Combining Molecular Dynamics and Quantitative Structure–Activity Relationship

Georgios Nikolaou, Antonios Koufou, Sotiria Anastasiou, Peter Roupas, Michail Chalaris
article en
1 citations

Abstract

This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve agents, both Ellison–Hoenig and Mirzayanov structural proposals were examined. MD simulations (10 ns, NPT ensemble) provided key thermodynamic properties, including density, molar heat capacity, and diffusivity. Simulated densities for G-agents (e.g., Sarin: 1.09 g/cm3, Soman: 1.03 g/cm3) and A-agents (e.g., A230: 1.608 g/cm3, Ellison–Hoenig model) closely matched experimental data. Heat capacities ranged from 258 to 462 J/mol·K, and self-diffusion coefficients revealed lower mobility for A-agents, especially under the Ellison–Hoenig configurations. QSAR modeling focused on lipophilicity (LogP) and acute toxicity (LD50). Predicted LD50 values ranged from 0.012 to 0.017 mg/kg for G-agents and up to 1.23 mg/kg for A-agents. A-234 showed the highest lipophilicity (LogP = 2.97) and toxicity (LD50 = 0.51 mg/kg) within its group. Additional descriptors, such as molecular weight and polar surface area, supported toxicity predictions. Strong correlations emerged between MD-derived properties and QSAR outputs, validating the integrated approach. The combined use of MD and QSAR techniques provided a comprehensive view of the agents’ environmental behavior and toxicological impact, supporting safer assessment strategies and reinforcing the importance of multidisciplinary modeling for chemical threat mitigation.

ChemEngineeringVol. 9(4)
Democritus University of Thrace (GR)
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
1.50
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