Construction of reliable QSPR models for predicting the impact sensitivity of nitroenergetic compounds using correlation weights of the fragments of molecular structures.

Impact sensitivity is a critical property of energetic molecules, indicating their tendency to react when subjected to mechanical stimuli such as impact. Nitro compounds are widely used as explosives across industrial, military, and civilian applications, making their safe handling a significant concern for engineers and scientists working with these materials. Predicting whether a molecule has the potential to pose safety risks is therefore of great importance. This study aimed to develop a QSPR model for predicting the impact sensitivity of 404 nitro compounds using the Monte Carlo algorithm implemented in CORAL-2023 software. The Simplified Molecular Input Line Entry System (SMILES) was employed to represent the molecular structures, while correlation weight descriptors were computed. Four target functions (TF0, TF1, TF2, and TF3) were used to generate robust models. The first model applied Monte Carlo optimization without the inclusion of IIC (information index of correlation) or CII (correlation index of information); the second incorporated IIC; the third incorporated CII; and the fourth applied both IIC and CII. Comparative statistical analyses indicated that the model integrating both IIC and CII demonstrated superior predictive performance, with the best results observed in split 2 (R