Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule‐based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads...

Full description

Saved in:
Bibliographic Details
Published in:Chemistry : a European journal 2017-05, Vol.23 (25), p.5966-5971
Main Authors: Segler, Marwin H. S., Waller, Mark P.
Format: Article
Language:English
Subjects:
artificial intelligence
Chemical reactions
Chemistry
Machine learning
Neural networks
Organic chemistry
retrosynthesis
synthesis design
total synthesis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule‐based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10‐accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions. Smart synthesis: Computers can be taught to perform retrosynthesis and reaction prediction with deep neural networks. The machine learns to prioritize rules and identify reactivity conflicts. This solves one of the main limitations of current systems for computer‐aided synthesis design.
ISSN:0947-6539
1521-3765
DOI:10.1002/chem.201605499