Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis
arxiv(2023)
摘要
Single-step retrosynthesis (SSR) in organic chemistry is increasingly
benefiting from deep learning (DL) techniques in computer-aided synthesis
design. While template-free DL models are flexible and promising for
retrosynthesis prediction, they often ignore vital 2D molecular information and
struggle with atom alignment for node generation, resulting in lower
performance compared to the template-based and semi-template-based methods. To
address these issues, we introduce Node-Aligned Graph-to-Graph (NAG2G), a
transformer-based template-free DL model. NAG2G combines 2D molecular graphs
and 3D conformations to retain comprehensive molecular details and incorporates
product-reactant atom mapping through node alignment which determines the order
of the node-by-node graph outputs process in an auto-regressive manner. Through
rigorous benchmarking and detailed case studies, we have demonstrated that
NAG2G stands out with its remarkable predictive accuracy on the expansive
datasets of USPTO-50k and USPTO-FULL. Moreover, the model's practical utility
is underscored by its successful prediction of synthesis pathways for multiple
drug candidate molecules. This not only proves NAG2G's robustness but also its
potential to revolutionize the prediction of complex chemical synthesis
processes for future synthetic route design tasks.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要