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dc.contributor.authorZheng, Shuangjia
dc.contributor.authorYan, Xin
dc.contributor.authorYang, Yuedong
dc.contributor.authorXu, Jun
dc.date.accessioned2019-06-10T01:36:31Z
dc.date.available2019-06-10T01:36:31Z
dc.date.issued2019
dc.identifier.issn1549-9596
dc.identifier.doi10.1021/acs.jcim.8b00803
dc.identifier.urihttp://hdl.handle.net/10072/384784
dc.description.abstractRecognizing substructures and their relations embedded in a molecular structure representation is a key process for structure–activity or structure–property relationship (SAR/SPR) studies. A molecular structure can be explicitly represented as either a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical properties, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance compared with state-of-the-art models. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design and synthesize the activity- or property-improved compounds.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAMER CHEMICAL SOC
dc.relation.ispartofpagefrom914
dc.relation.ispartofpageto923
dc.relation.ispartofissue2
dc.relation.ispartofjournalJOURNAL OF CHEMICAL INFORMATION AND MODELING
dc.relation.ispartofvolume59
dc.subject.fieldofresearchMedicinal and Biomolecular Chemistry
dc.subject.fieldofresearchTheoretical and Computational Chemistry
dc.subject.fieldofresearchComputation Theory and Mathematics
dc.subject.fieldofresearchcode0304
dc.subject.fieldofresearchcode0307
dc.subject.fieldofresearchcode0802
dc.titleIdentifying Structure-Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorYang, Yuedong


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