Abstract
Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. Quantum chemical modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectral prediction.1 To explore this technique further, we investigate the effects of including quantum chemically derived features as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from Extended Tight-Binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectral prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.
Competing Interest Statement
The authors have declared no competing interest.
