Nature Machine Intelligence | Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model
Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang & Zhenming Liu
Abstract:
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 new pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results in performance of challenging downstream tasks. By possessing chemical knowledge, our generative framework overcomes the limitations of current molecule generation models that rely on a small number of reaction templates. In extensive experiments, our model generates synthesizable drug-like structures of high quality. Overall, our work presents a noteworthy step toward a large-scale deep-learning framework for a variety of reaction-based applications.
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