# De-novo Identification of Small Molecules from Their GC-EI-MS Spectra

arXiv: https://arxiv.org/abs/2304.01634 · HTML: https://mlsanity.com/paper/2304.01634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Contrastive Search Is What You Need For Neural Text Generation](https://mlsanity.com/paper/2210.14140/) (2022)
- [Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks](https://mlsanity.com/paper/1811.08545/) (2018)

## Abstract (arXiv)

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 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.