QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction.

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. QC 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 spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information 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 spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

Effectiveness and Utility of Flowcharts on Learning in a Classroom Setting: A Mixed-Methods Study.

OBJECTIVE: Graphical representation of information organizes and promotes meaningful learning. As an example of graphical organizers, flowcharts can simplify and summarize complex information. The evidence of classroom use of flowcharts as an instructional tool is unclear. We investigated the effectiveness of flowcharts on student learning as an in-class instructional tool in a cardiovascular therapeutic course. Student experiences with the use and application of flowcharts were explored. METHODS: An explanatory sequential mixed-methods study was conducted with pharmacy students enrolled in an acute-care cardiovascular course from 2019-2021. The quantitative phase comprised a survey to determine flowchart effectiveness and a comparison of student performance in three content areas. The qualitative phase of the study used focused group interviews to understand student perceptions of flowchart use. RESULTS: Survey results indicated that using flowcharts improved understanding (110/128, 86%), integration of material (114/128, 89%), and overall knowledge (111/128, 87%). Student performance in the 3 content areas, shock, arrhythmia, and acute coronary syndrome were statistically significant with flowcharts implementation. Emerging themes from student interviews were (1) used as a medium for retention and recall, (2) used as a study tool, and (3) used as a decision-making framework. CONCLUSION: Flowcharts provide an alternative approach to teaching complex content, which allows students to organize and summarize information that promotes meaningful learning. The ease of implementation combined with the generalized nature of flowcharts makes it an effective graphical organizer that can be used across various disciplines.

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.

Tandem mass spectrometry (MS/MS) is a primary tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra data sets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (∼10% more spectra correctly identified) on large data sets (2000-10 000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50-90 eV), and with smaller data sets available through MassBank.