Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks.,Analytical Chemistry

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Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks.
Analytical Chemistry ( IF 6.7 ) Pub Date : 2020-06-17 , DOI: 10.1021/acs.analchem.0c01450
Hongchao Ji ₁ , Hanzi Deng ₁ , Hongmei Lu ₁ , Zhimin Zhang ₁

Affiliation

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.

中文翻译：

使用深层神经网络根据电子电离质谱预测分子指纹。

气相色谱法（GC）联用的电子电离质谱（EI-MS）是分析复杂样品中挥发性化合物的主要手段。光谱匹配方法只能识别光谱数据库中的化合物。作为回应，我们提出了一种基于深度学习的方法（DeepEI），以其EI-MS光谱解析未知化合物的结构。DeepEI利用深度神经网络从EI-MS光谱预测分子指纹，并使用预测的指纹搜索分子结构数据库。我们使用MassBank光谱评估了DeepEI，结果表明DeepEI是一种有效的识别方法。此外，DeepEI可以与数据库光谱匹配和NEIMS（指纹到光谱方法）配合使用，以提高识别准确性。

更新日期：2020-07-07

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