Computational chemical synthesis analysis and pathway design
- 1Peking University, China
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.
Keywords: Chemical synthesis analysis, Retrosynthesis, pathway design, deep learning, seq2seq
Received: 29 Jan 2018;
Accepted: 15 May 2018.
Edited by:Daniela Schuster, Paracelsus Medizinische Privatuniversität, Salzburg, Austria
Reviewed by:Dharmendra K. Yadav, Gachon University of Medicine and Science, South Korea
Mingyue Zheng, Shanghai Institute of Materia Medica (CAS), China
Copyright: © 2018 Pei, Feng and Lai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Jianfeng Pei, Peking University, Beijing, China, email@example.com