Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

在过去的十年中，非法药物市场因秘密生产的设计药物的扩散而发生了翻天覆地的变化。这些药物被称为新的精神活性物质 (NPS)，旨在模仿更广为人知的滥用药物的生理作用，同时避开药物管制法。NPS 滥用的公共卫生负担迫使毒理学、警察和海关实验室在执法缉获和生物样本中对其进行筛查。然而，由于这些物质的化学多样性以及它们在非法市场上出现的短暂性，对新兴 NPS 的识别具有挑战性。在这里，我们展示了 DarkNPS，这是一种支持深度学习的方法，仅使用质谱数据即可自动阐明身份不明的设计药物的结构。我们的方法采用深度生成模型来学习未观察到的结构的统计概率分布，我们称之为结构先验。我们表明，结构先验允许 DarkNPS 以 51% 的准确度和 86% 的 top-10 准确度阐明未识别 NPS 的确切化学结构。我们的生成方法有可能使质谱法常规分析的其他类型小分子的从头结构解析。