Computational Chemical Synthesis Analysis and Pathway Design

• Published in: Frontiers in Chemistry
• Main Authors: Fan Feng, Luhua Lai, Jianfeng Pei
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-06-01
• Subjects: chemical synthesis analysis; retrosynthesis; pathway design; deep learning; seq2seq
• Online Access: https://www.frontiersin.org/article/10.3389/fchem.2018.00199/full

With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules. Programs like ARChem Route Designer replaced hand-coded reaction modes with automatically-extracted rules, and programs like Chematica changed traditional designing into network searching. Afterward, with the help of machine learning, two-step models which combine reaction rules and statistical methods became the main stream. Recently, fully data-driven learning methods using deep neural networks which even do not require any prior knowledge, were applied into this field. Up to now, however, these methods still cannot replace experienced human organic chemists due to their relatively low accuracies. Future new algorithms with the aid of powerful computational hardware will make this topic promising and with good prospects.