Monte Carlo-based QSPR modeling of drop-weight impact sensitivity of energetic materials using hybrid SMILES–graph descriptors
- 1Department of Chemistry, Payame Noor University (PNU) 19395-4697 Tehran Iran
- 2Department of Chemistry, TeMS.C., Islamic Azad University Tehran Iran
- 3Department of Chemistry, Kurukshetra University Kurukshetra Haryana 136119 India
Abstract
Impact sensitivity under mechanical stimuli is one of the most crucial safety properties of energetic materials. The drop-weight test is a common method for evaluating the impact sensitivity of explosives, with results typically reported as the 50% initiation height (H50) or drop energy (E50 (cm)). In this study, three quantitative structure–property relationship (QSPR) models were developed and assessed to predict the logarithm drop-weight impact sensitivity (log E50) of various types of explosive materials using a Monte Carlo algorithm implemented via CORAL software. The descriptor of correlation weight (DCW) was computed from the SMILES notations of the compounds. The correlation intensity index (CII) and the index of ideality of correlation (IIC) were employed as supplementary parameters for optimizing the correlation weights (CW) utilized in the computation of optimal descriptors. A dataset of 515 explosives or energetic compounds was employed to make QSPR models utilizing hybrid optimal descriptors derived from SMILES and hydrogen filled graph (HFG). The dataset was partitioned into four subsets: active training, passive training, calibration, and validation. The prediction ability of generated models was evaluated by various validation criteria. For the log E50 endpoint, the models obtained across three independent random data splits achieved an average validation-set R2 of 0.7005 ± 0.0094. An average determination coefficient of 0.7162 ± 0.0129 was obtained for the active training sets. The structural attributes that promote an increase/decrease in log E50 were extracted and discussed, providing a mechanistic interpretation of the model.
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BibTeX
@article{lotfi2026monte,
title = {{Monte Carlo-based QSPR modeling of drop-weight impact sensitivity of energetic materials using hybrid SMILES–graph descriptors}},
author = {Shahram Lotfi and Shahin Ahmadi and Parvin Kumar},
journal = {RSC advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra07617c},
url = {https://europepmc.org/article/PMC/PMC13662854},
note = {CC BY},
}
RIS
TY - JOUR
TI - Monte Carlo-based QSPR modeling of drop-weight impact sensitivity of energetic materials using hybrid SMILES–graph descriptors
AU - Shahram Lotfi
AU - Shahin Ahmadi
AU - Parvin Kumar
PY - 2026
DA - 2026/10/14
JO - RSC advances
DO - 10.1039/d6ra07617c
UR - https://europepmc.org/article/PMC/PMC13662854
AB - Impact sensitivity under mechanical stimuli is one of the most crucial safety properties of energetic materials. The drop-weight test is a common method for evaluating the impact sensitivity of explosives, with results typically reported as the 50% initiation height (H50) or drop energy (E50 (cm)). In this study, three quantitative structure–property relationship (QSPR) models were developed and assessed to predict the logarithm drop-weight impact sensitivity (log E50) of various types of explosive materials using a Monte Carlo algorithm implemented via CORAL software. The descriptor of correlation weight (DCW) was computed from the SMILES notations of the compounds. The correlation intensity index (CII) and the index of ideality of correlation (IIC) were employed as supplementary parameters for optimizing the correlation weights (CW) utilized in the computation of optimal descriptors. A dataset of 515 explosives or energetic compounds was employed to make QSPR models utilizing hybrid optimal descriptors derived from SMILES and hydrogen filled graph (HFG). The dataset was partitioned into four subsets: active training, passive training, calibration, and validation. The prediction ability of generated models was evaluated by various validation criteria. For the log E50 endpoint, the models obtained across three independent random data splits achieved an average validation-set R2 of 0.7005 ± 0.0094. An average determination coefficient of 0.7162 ± 0.0129 was obtained for the active training sets. The structural attributes that promote an increase/decrease in log E50 were extracted and discussed, providing a mechanistic interpretation of the model.
SN - 2046-2069
ER -