Graph transformation policy network for chemical reaction prediction

Do, Kien, Tran, Truyen and Venkatesh, Svetha 2019, Graph transformation policy network for chemical reaction prediction, in KDD 2019 : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, New York, N.Y., pp. 750-760, doi: 10.1145/3292500.3330958.

Attached Files
Name Description MIMEType Size Downloads

Title Graph transformation policy network for chemical reaction prediction
Author(s) Do, Kien
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Knowledge Discovery & Data Mining. International Conference (25th : 2019 : Anchorage, Alaska)
Conference location Anchorage, Alaska
Conference dates 2019/08/04 - 2019/08/08
Title of proceedings KDD 2019 : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Publication date 2019
Start page 750
End page 760
Total pages 11
Publisher ACM
Place of publication New York, N.Y.
Summary © 2019 Copyright held by the owner/author(s). We address a fundamental problem in chemistry known as chemical reaction product prediction. Our main insight is that the input reactant and reagent molecules can be jointly represented as graphs, and the process of generating product molecules from reactant molecules can be formulated as a set of graph transformations. To this end, we propose Graph Transformation Policy Network (GTPN) - a novel generic method that combines the strengths of graph neural networks and reinforcement learning to learn reactions directly from data with minimal chemical knowledge. Compared to previous methods, GTPN has some appealing properties such as: end-to-end learning, and making no assumption about the length or the order of graph transformations. In order to guide our model search through the complex discrete space of sets of graph transformations effectively, we extend the standard policy gradient loss by adding useful constraints. Evaluation results show that GTPN improves the top-1 accuracy over the current state-of-the-art method by about 3% on the large USPTO dataset.
ISBN 9781450362016
Language eng
DOI 10.1145/3292500.3330958
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2019, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30129583

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 37 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 05 Sep 2019, 11:29:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.