Conclusions A novel approach for graph augmentation with NBO analysis-derived data was developed. In the first step, molecular graphs are extended with bond nodes and lone pair nodes, the number of which is computed by a graph neural network. The extended graphs are then used as an input for another model, which solved a multitask NBO analysis data prediction problem (node-level and link prediction tasks). Finally, the extended graphs are enriched with features that can be used to represent various downstream tasks. As an example, we show increased performance over baselines in predicting the properties of molecules from the QM9 dataset.