  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.
