  How to effectively represent molecules is a long-standing challenge for
molecular property prediction and drug discovery. This paper studies this
problem and proposes to incorporate chemical domain knowledge, specifically
related to chemical reactions, for learning effective molecular
representations. However, the inherent cross-modality property between chemical
reactions and molecules presents a significant challenge to address. To this
end, we introduce a novel method, namely MolKD, which Distills cross-modal
Knowledge in chemical reactions to assist Molecular property prediction.
Specifically, the reaction-to-molecule distillation model within MolKD
transfers cross-modal knowledge from a pre-trained teacher network learning
with one modality (i.e., reactions) into a student network learning with
another modality (i.e., molecules). Moreover, MolKD learns effective molecular
representations by incorporating reaction yields to measure transformation
efficiency of the reactant-product pair when pre-training on reactions.
Extensive experiments demonstrate that MolKD significantly outperforms various
competitive baseline models, e.g., 2.1% absolute AUC-ROC gain on Tox21. Further
investigations demonstrate that pre-trained molecular representations in MolKD
can distinguish chemically reasonable molecular similarities, which enables
molecular property prediction with high robustness and interpretability.
