We boost masked graph autoencoders for molecules by introducing DyCC, a dynamic, chemistry-aware training framework with adaptive masking (GIBMS) and soft label reconstruction (SLG).

S²VM self-supervised learning from large unlabeled drug pairs, achieving SOTA DDI prediction and superior generalization/interpretability on novel/few-shot drugs.