TY - JOUR
T1 - Application of Modern Intelligent Algorithms in Retrosynthesis Prediction
AU - Liao , Jianhan
AU - Shi , Xiaoxin
AU - Gao , Ya
AU - Wang , Xingyu
AU - Zhu , Tong
JO - Communications in Computational Chemistry
VL - 4
SP - 289
EP - 310
PY - 2025
DA - 2025/10
SN - 7
DO - http://doi.org/10.4208/cicc.2025.153.01
UR - https://global-sci.org/intro/article_detail/cicc/24507.html
KW - artificial intelligence, retrosynthesis prediction, machine learning, deep learning.
AB - <p>In recent years, the rapid advancements in computer science have spurred the development of various cutting-edge intelligent algorithms. Among these, the transformer, which is built upon a multi-head attention mechanism, is one of the most prominent AI models. The advent of such algorithms has significantly advanced retrosynthesis prediction, though challenges remain in chemical interpretability and real-world deployment. Unlike traditional models, AI-based retrosynthesis prediction systems can automatically extract chemical knowledge from vast datasets to forecast retrosynthesis pathways. This review provides a comprehensive overview of modern intelligent algorithms applied to retrosynthesis prediction, with a particular focus on artificial intelligence techniques. We begin by discussing key deep learning models, then explore available chemical reaction datasets and molecular representations. The discussion extends to the latest state-of-the art in AI-assisted retrosynthesis models, including template-based, template-free, and semi-template-based approaches. Finally, we compare these models across various classifications, highlighting several challenges and limitations of current methods, and suggesting promising directions for future research.</p>