Screening for NBOMe Hallucinogens based on Artificial Neural Networks and Structural Descriptors

Abstract

Synthetic hallucinogen trafficking is a global illicit trade. It represents one of the main dangers to public health worldwide. NBOMe is a new class of synthetic hallucinogenic drugs of abuse, which is sold through illicit channels as alternative to LSD. The most representative member of the NBOMe class is 25I-NBOMe, a derivative of 2,5-dimethoxy-4-iodophenetylamine (2C-I). In this study we are presenting and comparing a series of Artificial Neural Networks (ANNs) designed to identify NBOMe hallucinogens based on their structural descriptors. Such a system may automatically predict the potential toxicity of new NBOMe compounds and thus saves analytical time and reduces the cost of toxicity studies. For this purpose, constitutional descriptors and functional groups of the optimized molecular structures of the main NBOMe hallucinogens have been determined. Then ANNs have been built by using only those descriptors found to be the most important structural descriptors. The efficiency of the ANNS was compared and the impact of variable selection on ANN performance was analyzed in detail based on several merit figures.