Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
This work addresses the need for a holistic deep-learning approach in drug design and organic chemistry, though it appears incremental by building on existing methods.
The paper tackles the problem of integrating chemical reaction pretraining with conditional molecule generation by proposing a unified deep-learning framework, achieving state-of-the-art results on downstream tasks and generating high-quality, synthesizable drug-like structures.
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.