Predicting the Thermal Stability of Nitroaromatic Compounds Using Chemoinformatic Tools.

Area of Science:

• Computational chemistry
• Chemical hazard assessment
• Regulatory science

Background:

• European REACH regulation prioritizes toxicological and ecotoxicological data.
• Explosive properties of chemicals, a key hazard, receive less regulatory attention.
• Nitroaromatic compounds pose significant chemical hazards requiring accurate property prediction.

Purpose of the Study:

• To develop novel quantitative structure-property relationship (QSAR) models for predicting the heat of decomposition in nitroaromatic compounds.
• To create predictive models adhering to OECD principles for QSAR validation.
• To assess the reliability, robustness, and applicability domain of the developed QSAR models.

Main Methods:

• Utilized chemoinformatic tools including Partial Least Squares (PLS), Multilinear Regression (MLR), and Decision Trees.
• Developed and validated three distinct QSAR models.
• Performed internal and external validation, including cross-validation, and defined applicability domains.

Main Results:

• Successfully developed three robust QSAR models (MLR, PLS, Decision Tree) for predicting heat of decomposition.
• Models demonstrated strong predictive power on external validation sets.
• Established clear applicability domains for the models, primarily within nitrobenzene derivatives.

Conclusions:

• The developed QSAR models are reliable, robust, and possess good predictive capabilities for nitroaromatic compound decomposition heat.
• These models are easily interpretable and suitable for regulatory applications concerning chemical hazards.
• The study highlights the utility of chemoinformatics in addressing under-researched chemical hazard properties.