A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges

TL;DR

This paper provides a comprehensive survey of deep learning methods for reaction and retrosynthesis prediction, highlighting current approaches, limitations, challenges, and future research directions in computational chemistry.

Contribution

It offers the first unified, systematic review of deep learning models for reaction and retrosynthesis prediction, analyzing their mechanisms, strengths, and weaknesses.

Findings

Summarizes state-of-the-art deep learning approaches

Identifies limitations and open challenges

Suggests future research directions

Abstract

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.