The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

Currently, the available spectroscopic data on Novichok candidates are limited because experimental measurements are difficult to conduct due to the toxicity of these candidates. Furthermore, the Chemical Weapons Convention (CWC)...

Following the recent terrorist attacks using Novichok agents and the subsequent decomposition operations, understanding the chemical structures of nerve agents has become important. To mitigate the ever-evolving threat of new variants, the Organization for the Prohibition of Chemical Weapons has updated the list of Schedule 1 substances defined by the Chemical Weapons Convention. However, owing to the several possible structures for each listed substance, obtaining an exhaustive dataset is almost impossible. Therefore, we propose a nuclear magnetic resonance-based prediction method for 1H and 13C NMR chemical shifts of Novichok agents based on conformational and density functional study calculations. Four organophosphorus compounds and five G- and V-type nerve agents were used to evaluate the accuracy of the proposed procedure. Moreover, 1H and 13C NMR prediction results for an additional 83 Novichok candidates were compiled as a database to aid future research and identification. Further, this is the first study to successfully predict the NMR chemical shifts of Novichok agents, with an exceptional agreement between predicted and experimental data. The conclusions enable the prediction of all possible structures of Novichok agents and can serve as a firm foundation for preparation against future terrorist attacks using new variants of nerve agents.