Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/74509

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dc.contributor.authorSousa, Tiagopor
dc.contributor.authorCorreia, Joãopor
dc.contributor.authorPereira, Vítorpor
dc.contributor.authorRocha, Miguelpor
dc.date.accessioned2021-10-25T10:48:50Z-
dc.date.issued2021-04-
dc.identifier.citationSousa, Tiago; Correia, João; Pereira, Vítor; Rocha, Miguel, Combining multi-objective evolutionary algorithms with deep generative models towards focused molecular design. Lecture Notes in Computer Science. Vol. 12694, Germany, Springer Verlag, 81-96, 2021. DOI: 10.1007/978-3-030-72699-7_6por
dc.identifier.isbn9783030726980por
dc.identifier.issn0302-9743por
dc.identifier.urihttps://hdl.handle.net/1822/74509-
dc.description.abstractRecent advances in applying deep generative learning to molecular design have led to a large number of novel approaches to the targeted generation of molecules towards specific features and applications. In this work, we expand on the latent space navigation approach, where molecules are optimized by operating in their latent representation inside a deep auto-encoder, by introducing multi-objective evolutionary algorithms (MOEAs), and benchmarking the proposed framework on several objectives from recent literature. Using several case studies from literature, we show that our proposed method is capable of controlling abstract chemical properties, is competitive with other state-of-the-art methods and can perform relevant tasks such as optimizing a predefined molecule while maintaining a similarity threshold. Also, MOEAs allow to generate molecules with a good level of diversity, which is a desired feature.por
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement number 814408).por
dc.language.isoengpor
dc.publisherSpringer Verlagpor
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/814408/EUpor
dc.rightsrestrictedAccesspor
dc.subjectMolecular designpor
dc.subjectDeep generative modelspor
dc.subjectMulti-objective evolutionary algorithmspor
dc.titleCombining multi-objective evolutionary algorithms with deep generative models towards focused molecular designpor
dc.typeconferencePaper-
dc.peerreviewedyespor
dc.relation.publisherversionhttp://www.springer.com/computer/lncs?SGWID=0-164-0-0-0por
dc.commentsCEB54473por
oaire.citationStartPage81por
oaire.citationEndPage96por
oaire.citationConferencePlaceGermany-
oaire.citationVolume12694-
dc.date.updated2021-10-09T11:44:36Z-
dc.identifier.doi10.1007/978-3-030-72699-7_6por
dc.date.embargo10000-01-01-
dc.subject.fosCiências Naturais::Ciências Biológicaspor
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersion-
sdum.journalLecture Notes in Computer Sciencepor
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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