Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks
Year: 2020
Type: article
Abstract: Electron ionization-mass spectrometry (EI-MS) hyphenated to gas chromatography (GC) is the workhorse for analyzing volatile compounds in complex samples. The spectral matching method can only identify compounds within the spectral database. In response, we present a deep-learning-based approach (DeepEI) for structure elucidation of an unknown compound with its EI-MS spectrum. DeepEI employs deep neural networks to predict molecular fingerprints from an EI-MS spectrum and searches the molecular structure database with the predicted fingerprints. We evaluated DeepEI with MassBank spectra, and the results indicate DeepEI is an effective identification method. In addition, DeepEI can work cooperatively with database spectral matching and NEIMS (fingerprint to spectrum method) to improve identification accuracy.
Source: Analytical Chemistry
Authors: Hongchao Ji, Hanzi Deng, Hongmei Lu, Zhimin Zhang
Institution: Central South University
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: bronze
Awards: 代谢组学数据高性能分析方法与整体策略研究, G8299490654
OpenAlex ID: W3036061391