Deep learning in retrosynthesis planning: datasets, models and tools

In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning.

Automated summary

This review comprehensively summarizes the development process of retrosynthesis in the context of deep learning, covering aspects such as datasets, models, and tools. Representative models from academia and available platforms in the industry are discussed. The review also addresses the limitations of existing models and provides potential future trends for beginners to understand and participate in retrosynthesis planning.