Link prediction approach Orbital interactions data is not available directly from the molecular structure, so it should be predicted first. Therefore, the problem was formulated as a link prediction task, which is essentially a classification problem. "Positive examples" (i.e., cases where there is an interaction) were taken from the original dataset, while "negative examples" were sampled from other possible combinations of bonds and lone pairs. Moreover, the direction is essential as (in our case) it describes the donor-acceptor pair but not vice versa. Input data consisted of concatenated embeddings of corresponding nodes and dynamically calculated pairwise features. We performed the training with binary cross-entropy loss. Standard classification metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC AUC) were calculated.