Title: Distilling Pre-trained Knowledge in Chemical Reactions for Molecular Property Prediction. Abstract: How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery. Recently, accumulative unlabelled molecule data have spurred the rapid development of pre-training methods for molecular representation learning. However, these works mainly focus on devising self-supervised learning tasks and/or introducing 3D geometric information based on molecular structures with little chemical domain knowledge involved. To address this issue, we propose a novel method (MolKD) by Distilling pre-trained Knowledge in chemical reactions to assist Molecular property prediction. Specifically, MolKD first learns effective representations by incorporating reaction yields to measure transformation efficiency of the reactant-product pair when pre-training on reactions. Next, MolKD introduces the reaction-to-molecule distillation to transfer cross-modal knowledge between pre-training chemical reaction data and the downstream molecular property prediction tasks. Extensive experiments show that our method can learn effective molecular representations, achieving superior performance compared with state-of-the-art baselines, e.g., 2.8% absolute Hit@1 gain on USPTO in chemical reaction prediction and 1.6% absolute AUC-ROC gain on Tox21 with 1/3 pre-training data size in molecular property prediction. Further investigations on pre-trained molecular representations indicate that MolKD learns to distinguish chemically meaningful molecular similarities, which enables molecular property prediction with high robustness and interpretability.