  Chemical reactions are the fundamental building blocks of drug design and
organic chemistry research. In recent years, there has been a growing need for
a large-scale deep-learning framework that can efficiently capture the basic
rules of chemical reactions. In this paper, we have proposed a unified
framework that addresses both the reaction representation learning and molecule
generation tasks, which allows for a more holistic approach. Inspired by the
organic chemistry mechanism, we develop a novel pretraining framework that
enables us to incorporate inductive biases into the model. Our framework
achieves state-of-the-art results on challenging downstream tasks. By
possessing chemical knowledge, our generative framework overcome the
limitations of current molecule generation models that rely on a small number
of reaction templates. In the extensive experiments, our model generates
synthesizable drug-like structures of high quality. Overall, our work presents
a significant step toward a large-scale deep-learning framework for a variety
of reaction-based applications.
