title: QSPR studies of impact sensitivity of nitro energetic compounds using three-dimensional descriptors.
abstract: The quantitative structure-property relationship (QSPR) studies were performed between molecular structures and impact sensitivity for a diverse set of nitro energetic compounds based on three-dimensional (3D) descriptors. The entire set of 156 compounds was divided into a training set of 127 compounds and a test set of 29 compounds according to Kennard and Stones algorithm. Multiple linear regression (MLR) analysis was employed to select the best subset of descriptors and to build linear models; while nonlinear models were developed by means of artificial neural network (ANN). The obtained models with ten descriptors involved show good predictive power for the test set: a squared correlation coefficient (R²) of 0.7222 and a standard error of estimation (s) of 0.177 were achieved by the MLR model; while by the ANN model, R² and s were 0.8658 and 0.130, respectively. Therefore, the proposed models can be used to predict the impact sensitivity of new nitro compounds for engineering.
authors.affiliation: College of Materials Science & Engineering, Wuhan Textile University, 430073 Wuhan, China. xujie0@ustc.edu
authors.forename: Jie
authors.initials: J
authors.lastname: Xu
authors.forename: Ligen
authors.initials: L
authors.lastname: Zhu
authors.forename: Dong
authors.initials: D
authors.lastname: Fang
authors.forename: Luoxin
authors.initials: L
authors.lastname: Wang
authors.forename: Shili
authors.initials: S
authors.lastname: Xiao
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.forename: Weilin
authors.initials: W
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D009574:Nitro Compounds / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D009574:Nitro Compounds