Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach

Abstract

Infrared spectroscopy stands as a formidable analytical tool, ubiquitously employed across materials science and biomedical research. Yet, manual interpretation of spectral data remains a laborious and time-intensive endeavor. Carbonyl groups, pervasive in organic compounds, such as natural products, pharmaceuticals, and peptides, demonstrate both compelling predictive potential and critical diagnostic significance for interpretable spectral analysis. This work harnesses experimentally measured spectroscopic data from chemical literature, constructing a robust data set to propel efficiency. We introduce NE-GNN, a specialized feature extraction approach synergizing proximal structural analysis with graph neural networks─a method that marries predictive accuracy with interpretability. By leveraging this chemically intuitive framework, NE-GNN achieves remarkable precision in predicting carbonyl group peaks in infrared spectra. Beyond accelerating machine learning applications in spectroscopic analysis, this strategy supports data-driven discovery in chemistry.