Jeong_2022_Chem.Res.Toxicol_35_774

Reference

Title : Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update - Jeong_2022_Chem.Res.Toxicol_35_774
Author(s) : Jeong K , Lee JY , Woo S , Kim D , Jeon Y , Ryu TI , Hwang SR , Jeong WH
Ref : Chemical Research in Toxicology , 35 :774 , 2022
Abstract :

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.

PubMedSearch : Jeong_2022_Chem.Res.Toxicol_35_774
PubMedID: 35317551

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Citations formats

Jeong K, Lee JY, Woo S, Kim D, Jeon Y, Ryu TI, Hwang SR, Jeong WH (2022)
Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update
Chemical Research in Toxicology 35 :774

Jeong K, Lee JY, Woo S, Kim D, Jeon Y, Ryu TI, Hwang SR, Jeong WH (2022)
Chemical Research in Toxicology 35 :774