Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163080
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dc.contributor.authorLin, Min Htooen_US
dc.contributor.authorTu, Zhengkaien_US
dc.contributor.authorColey, Connor W.en_US
dc.date.accessioned2022-11-21T02:06:17Z-
dc.date.available2022-11-21T02:06:17Z-
dc.date.issued2022-
dc.identifier.citationLin, M. H., Tu, Z. & Coley, C. W. (2022). Improving the performance of models for one-step retrosynthesis through re-ranking. Journal of Cheminformatics, 14(1), 15-. https://dx.doi.org/10.1186/s13321-022-00594-8en_US
dc.identifier.issn1758-2946en_US
dc.identifier.urihttps://hdl.handle.net/10356/163080-
dc.description.abstractRetrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel machine learning approaches for data-driven synthesis planning. These methods learn complex patterns from reaction databases in order to predict, for a given product, sets of reactants that can be used to synthesise that product. However, their performance as measured by the top-N accuracy in matching published reaction precedents still leaves room for improvement. This work aims to enhance these models by learning to re-rank their reactant predictions. Specifically, we design and train an energy-based model to re-rank, for each product, the published reaction as the top suggestion and the remaining reactant predictions as lower-ranked. We show that re-ranking can improve one-step models significantly using the standard USPTO-50k benchmark dataset, such as RetroSim, a similarity-based method, from 35.7 to 51.8% top-1 accuracy and NeuralSym, a deep learning method, from 45.7 to 51.3%, and also that re-ranking the union of two models' suggestions can lead to better performance than either alone. However, the state-of-the-art top-1 accuracy is not improved by this method.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Cheminformaticsen_US
dc.rights© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en_US
dc.subjectScience::Chemistryen_US
dc.titleImproving the performance of models for one-step retrosynthesis through re-rankingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1186/s13321-022-00594-8-
dc.description.versionPublished versionen_US
dc.identifier.pmid35292121-
dc.identifier.scopus2-s2.0-85127152962-
dc.identifier.issue1en_US
dc.identifier.volume14en_US
dc.identifier.spage15en_US
dc.subject.keywordsComputer-Aided Synthesis Planningen_US
dc.subject.keywordsEnergy-Based Modelen_US
dc.description.acknowledgementThis work was supported by the CN Yang Scholars Programme at Nanyang Technological University and the Machine Learning for Pharmaceutical Discovery and Synthesis consortium.en_US
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