title: Predicting the impact sensitivity of explosive molecules using neuromimetic networks.
abstract: A new method for predicting the impact sensitivity of explosive molecules is presented. This method makes use of a network of formal neurons. The experiment uses 124 molecules belonging to different families. The molecular descriptors taken into account are the molecule's oxygen balance and the enumeration of certain groups. The results obtained are satisfactory: 80% of the molecules are correctly classed on a scale of four sensitivities. Comparison with a multivariate linear regression analysis gives a slight advantage to the neural network method.
authors.affiliation: Laboratoire de Modélisation et Simulation Appliquées à la Chimie, ENSCP, Paris, France.
authors.forename: H
authors.initials: H
authors.lastname: Nefati
authors.forename: B
authors.initials: B
authors.lastname: Diawara
authors.forename: J J
authors.initials: JJ
authors.lastname: Legendre
mesh_terms: D003198:Computer Simulation; D016014:Linear Models; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D009574:Nitro Compounds / Q000737:chemistry* / Q000494:pharmacology; D010100:Oxygen / Q000737:chemistry / Q000378:metabolism; D013329:Structure-Activity Relationship
chemical_list: D009574:Nitro Compounds; D010100:Oxygen