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dc.contributor.authorTu, Zhengkai
dc.contributor.authorStuyver, Thijs
dc.contributor.authorColey, Connor W
dc.date.accessioned2023-03-09T19:52:37Z
dc.date.available2023-03-09T19:52:37Z
dc.date.issued2023-01-04
dc.identifier.urihttps://hdl.handle.net/1721.1/148455
dc.description.abstractThe field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.en_US
dc.language.isoen
dc.publisherRoyal Society of Chemistry (RSC)en_US
dc.relation.isversionof10.1039/d2sc05089gen_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceRoyal Society of Chemistry (RSC)en_US
dc.titlePredictive chemistry: machine learning for reaction deployment, reaction development, and reaction discoveryen_US
dc.typeArticleen_US
dc.identifier.citationTu, Zhengkai, Stuyver, Thijs and Coley, Connor W. 2023. "Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery." Chemical Science, 14 (2).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineeringen_US
dc.relation.journalChemical Scienceen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-03-09T19:49:22Z
dspace.orderedauthorsTu, Z; Stuyver, T; Coley, CWen_US
dspace.date.submission2023-03-09T19:49:24Z
mit.journal.volume14en_US
mit.journal.issue2en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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