Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model

• URL: http://arxiv.org/abs/2303.06965v5
• Date: Thu, 7 Mar 2024 14:51:12 GMT
• Title: Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
• Authors: Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang, Zhenming Liu
• Abstract summary: We propose a unified framework that addresses both the reaction representation learning and molecule generation tasks. 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.
• Score: 3.3031562864527664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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