Clustering of Synthetic Routes Using Tree Edit Distance

Author(s):  
Samuel Genheden ◽  
Ola Engkvist ◽  
Esben Bjerrum
2020 ◽  
Author(s):  
Samuel Genheden ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum

<div>We present a novel algorithm to compute the distance between synthesis routes based on a tree edit distance calculation. Such distances can be used to cluster synthesis routes from a retrosynthesis prediction tool. We show that the clustering of routes from a retrosynthesis analysis is performed in less than ten seconds on average, and only constitutes seven percent of the total time (prediction + clustering). Furthermore, we are able to show that representative routes from each cluster can be used to reduce the set of predicted routes. Finally, we show with a number of examples that the algorithm gives intuitive clusters that can be easily rationalized. The algorithm is included in the latest version of the open-source AiZynthFinder software.</div>


2020 ◽  
Author(s):  
Samuel Genheden ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum

<div>We present a novel algorithm to compute the distance between synthesis routes based on a tree edit distance calculation. Such distances can be used to cluster synthesis routes from a retrosynthesis prediction tool. We show that the clustering of routes from a retrosynthesis analysis is performed in less than ten seconds on average, and only constitutes seven percent of the total time (prediction + clustering). Furthermore, we are able to show that representative routes from each cluster can be used to reduce the set of predicted routes. Finally, we show with a number of examples that the algorithm gives intuitive clusters that can be easily rationalized. The algorithm is included in the latest version of the open-source AiZynthFinder software.</div>


2020 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Karl Bringmann ◽  
Paweł Gawrychowski ◽  
Shay Mozes ◽  
Oren Weimann

2009 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Erik D. Demaine ◽  
Shay Mozes ◽  
Benjamin Rossman ◽  
Oren Weimann

2013 ◽  
Vol 48 ◽  
pp. 1-22 ◽  
Author(s):  
M. Alabbas ◽  
A. Ramsay

Many natural language processing (NLP) applications require the computation of similarities between pairs of syntactic or semantic trees. Many researchers have used tree edit distance for this task, but this technique suffers from the drawback that it deals with single node operations only. We have extended the standard tree edit distance algorithm to deal with subtree transformation operations as well as single nodes. The extended algorithm with subtree operations, TED+ST, is more effective and flexible than the standard algorithm, especially for applications that pay attention to relations among nodes (e.g. in linguistic trees, deleting a modifier subtree should be cheaper than the sum of deleting its components individually). We describe the use of TED+ST for checking entailment between two Arabic text snippets. The preliminary results of using TED+ST were encouraging when compared with two string-based approaches and with the standard algorithm.


Sign in / Sign up

Export Citation Format

Share Document