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

TitleCombining multi-objective evolutionary algorithms with deep generative models towards focused molecular design
Author(s)Sousa, Tiago
Correia, João
Pereira, Vítor
Rocha, Miguel
KeywordsMolecular design
Deep generative models
Multi-objective evolutionary algorithms
Issue dateApr-2021
PublisherSpringer Verlag
JournalLecture Notes in Computer Science
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_6
Abstract(s)Recent 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.
TypeConference paper
URIhttps://hdl.handle.net/1822/74509
ISBN9783030726980
DOI10.1007/978-3-030-72699-7_6
ISSN0302-9743
Publisher versionhttp://www.springer.com/computer/lncs?SGWID=0-164-0-0-0
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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