Automatic identification of NBOMe illicit psychoactive substances based on combined molecular descriptors

During the last decade, a growing prevalence of new psychoactive substances (NPS) has been noticed by the law enforcement agencies. Although NPS have no medical use due to their very high toxicity, they are often sold on the black market. NBOMe defines a group of toxic amphetamines that has as parent compound 25I-NBOMe, a synthetic derivative of 2C-I (2,5-dimethoxy-4-iodophenetylamine). In this paper, we are presenting a series of Artificial Neural Networks (ANNs) designed to identify the NBOMe class membership based on a mixture of topological and 3D-MoRSE descriptors. For this purpose, the molecular structures of 160 compounds representing NBOMe compounds, narcotics, sympathomimetic amines, potent analgesics, as well as their main precursors have been first optimized. Then a molecular database was formed by computing a large number of topological and 3D-MoRSE descriptors that characterize these structures. This database was used as input for building an ANN system designed to recognize NBOMes. The relevance of the input variables on its classification performance has been assessed and new systems have been built by using different combinations of selected topological and 3D-MoRSE descriptors. The best performing system has been found by comparing various classification efficiency criteria.