Identification of experimentally acquired mass spectra of unknown compounds presents a~particular challenge because reliable spectral databases do not cover the potential chemical space with sufficient density. Therefore machine learning based \emph{de-novo} methods, which derive molecular structure directly from its mass spectrum gained attention recently. We present a~novel method in this family, addressing a~specific usecase of GC-EI-MS spectra, which is particularly hard due to lack of additional information from the first stage of MS/MS experiments, on which the previously published methods rely. We analyze strengths and drawbacks or our approach and discuss future directions.

翻译：实验获取的未知化合物质谱图鉴定面临特殊挑战，因为可靠的谱图数据库未能以足够密度覆盖潜在化学空间。因此，近年来基于机器学习的从头方法（直接从质谱图推导分子结构）备受关注。我们提出该领域中的一种新方法，针对GC-EI-MS谱图的特定应用场景——由于缺乏先前发表方法所依赖的串联质谱第一级附加信息，该场景尤为困难。我们分析了该方法的优势与不足，并探讨了未来发展方向。