A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
It provides a comprehensive review for researchers in computational chemistry and drug discovery, but is incremental as it synthesizes existing work.
This survey paper tackles the problem of summarizing and analyzing deep learning approaches for reaction and retrosynthesis prediction in computational chemistry, providing a unified view of current methods and future challenges without presenting new experimental results.
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these problems, and some have achieved initial success. In this survey, we conduct a comprehensive investigation of advanced deep learning-based models for reaction and retrosynthesis prediction. We summarize the design mechanisms, strengths, and weaknesses of state-of-the-art approaches. Then, we discuss the limitations of current solutions and open challenges in the problem itself. Finally, we present promising directions to facilitate future research. To our knowledge, this paper is the first comprehensive and systematic survey that seeks to provide a unified understanding of reaction and retrosynthesis prediction.