  Molecular representation learning (MRL) is a fundamental task for drug
discovery. However, previous deep-learning (DL) methods focus excessively on
learning robust inner-molecular representations by mask-dominated pretraining
framework, neglecting abundant chemical reactivity molecular relationships that
have been demonstrated as the determining factor for various molecular property
prediction tasks. Here, we present MolCAP to promote MRL, a graph pretraining
Transformer based on chemical reactivity (IMR) knowledge with prompted
finetuning. Results show that MolCAP outperforms comparative methods based on
traditional molecular pretraining framework, in 13 publicly available molecular
datasets across a diversity of biomedical tasks. Prompted by MolCAP, even basic
graph neural networks are capable of achieving surprising performance that
outperforms previous models, indicating the promising prospect of applying
reactivity information for MRL. In addition, manual designed molecular templets
are potential to uncover the dataset bias. All in all, we expect our MolCAP to
gain more chemical meaningful insights for the entire process of drug
discovery.
