ИСТИНА

Two models are introduced: a XGBoost model trained on Morgan fingerprints and a graph neural network (GNN) that employs molecular graph representations. Charged molecules are treated by incorporating one-hot or learnable NN encoding to molecular representations. Both models demonstrate excellent predictive capabilities, for the first time enabling fast and accurate prediction of charged PAHs IR spectra. While the XGBoost model demonstrates the highest accuracy achieved up to date, the GNN shows significant promise for future advancements due to the inherent capabilities of molecular graph representations. Remaining challenges, such as scarcity of data on heteroatomic PAHs, and potential approaches of addressing them are also discussed in the manuscript.