Machine learning-based q-RASPR predictions of detonation heat for nitrogen-containing compounds

The quantitative Read-Across Structure-Property Relationship (q-RASPR) is a novel method for the property predictions derived from the integrated concept of both similarity-based predictions ( i.e. , Read-Across or RA) and statistical modelling-based predictions ( i.e. , Quantitative Structure-Property Relationship or QSPR). The main performance index of ammunition used in air-to-air and underwater weapons is the detonation heat energy. In the present work, we have applied the q-RASPR modeling approach and various Machine Learning (ML) algorithms to predict the detonation heat (an intrinsic property) of different N-containing compounds. The data set was collected from the literature, curated, and further divided into training and test sets using the Euclidean distance-based algorithm. The feature selection was done on the basis of internal validation metrics of Genetic Algorithm (GA) models. A Multiple Linear Regression (MLR) QSPR model with 6 descriptors was selected, and the model features were used to calculate the similarity and error-based RASPR descriptors. The RASPR descriptor matrix was then merged with the features of the QSPR model. A grid search was performed for the selection of a combination of descriptors which were then subjected to Partial Least Squares (PLS) regression to obviate the inter-correlation among the descriptors. We have also employed various ML algorithms by optimizing the hyperparameters based on a cross-validation approach and compared the final test set prediction results. The PLS q-RASPR model was selected to be the best model based on the external validation metrics and it also shows enhanced prediction quality using 2D-descriptors compared to the previous model reported with 3D-descriptors. The developed model can be used for the detection of the detonation heat of compounds containing nitrogen with an effective performance. The study aims to predict the detonation heat of different classes of nitrogen-containing compounds by utilizing various in silico approaches such as QSPR, Read-across, q-RASPR, and ML.