人工智能在化学逆合成中的应用

Artificial Intelligence for Retrosynthesis Prediction

近年来,人们对通过人工智能(AI)技术解决化学逆合成预测问题产生了巨大的兴趣。与化学家和基于规则的专家系统进行的逆合成预测不同,AI驱动的逆合成预测自动从现成的实验数据集中学习化学知识,以预测反应和逆合成路径。这提供了解决许多传统挑战的机会,包括专业知识过于繁杂、路线的长度过大和计算成本高。本文描述了当前AI驱动的逆合成预测的概况。我们首先讨论了化学逆合成的数学定义,并回顾了这个问题中的研究挑战。然后,我们回顾相关的AI技术和最新进度,以实现逆合成预测。此外,我们提供了逆合成系统不同部分的全面分类,并调查了AI方法如何重塑每个子模块。最后,我们讨论了未来有希望的研究领域。

In recent years,there has been a dramatic rise in interest in retrosynthesis prediction with artificial intelligence(AI)techniques.Unlike conventional retrosynthesis prediction performed by chemists and by rule-based expert systems,AI-driven retrosynthesis prediction automatically learns chemistry knowledge from off-the-shelf experimental datasets to predict reactions and retrosynthesis routes.This provides an opportunity to address many conventional challenges,including heavy reliance on extensive expertise,the sub-optimality of routes,and prohibitive computational cost.This review describes the current landscape of AI-driven retrosynthesis prediction.We first discuss formal definitions of the retrosynthesis problem and review the outstanding research challenges therein.We then review the related AI techniques and recent progress that enable retrosynthesis prediction.Moreover,we propose a novel landscape that provides a comprehensive categorization of different retrosynthesis prediction components and survey how AI reshapes each component.We conclude by discussing promising areas for future research.