# Hybrid Deep Learning Model for EI-MS Spectra Prediction

## TL;DR

This paper introduces a hybrid deep learning model that predicts EI-MS spectra from molecular structures, improving library coverage and reducing the need for experimental data.

## Contribution

A novel hybrid deep learning model combining graph and residual neural networks for EI-MS spectra prediction.

## Key findings

- The model achieves a Recall@10 of ≈80.8% on the NIST14 database.
- Generated spectra show high similarity to real ones, aiding library augmentation.
- Remaining challenges include generalization and ensuring spectral uniqueness.

## Abstract

Electron ionization (EI) mass spectrometry (MS) is a widely used technique for the compound identification and production of spectra. However, incomplete coverage of reference spectral libraries limits reliable analysis of newly characterized molecules. This study presents a hybrid deep learning model for predicting EI-MS spectra directly from molecular structure. The approach combines a graph neural network encoder with a residual neural network decoder, followed by refinement using cross-attention, bidirectional prediction, and probabilistic, chemistry-informed masks. Trained on the NIST14 EI-MS database (≤500 Da), the model achieves strong library matching performance (Recall@10 ≈ 80.8%) and high spectral similarity. The proposed hybrid GNN (Graph Neural Network)-ResNet (Residual Neural Network) model can generate high-quality synthetic EI-MS spectra to supplement existing libraries, potentially reducing the cost and effort of experimental spectrum acquisition. The obtained results demonstrate the potential of data-driven models to augment EI-MS libraries, while highlighting remaining challenges in generalization and spectral uniqueness.