DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Juhwan | ko |
dc.contributor.author | Jeong, Dae-Woong | ko |
dc.contributor.author | Kim, Kiyoung | ko |
dc.contributor.author | Han, Se-Hui | ko |
dc.contributor.author | Lee, Moontae | ko |
dc.contributor.author | Lee, Honglak | ko |
dc.contributor.author | Jung, Yousung | ko |
dc.date.accessioned | 2023-09-14T09:00:37Z | - |
dc.date.available | 2023-09-14T09:00:37Z | - |
dc.date.created | 2023-09-14 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.citation | 39th International Conference on Machine Learning, ICML 2022, pp.16952 - 16968 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312633 | - |
dc.description.abstract | Computational chemistry aims to autonomously design specific molecules with target functionality. Generative frameworks provide useful tools to learn continuous representations of molecules in a latent space. While modelers could optimize chemical properties, many generated molecules are not synthesizable. To design synthetically accessible molecules that preserve main structural motifs of target molecules, we propose a reaction-embedded and structure-conditioned variational autoencoder. As the latent space jointly encodes molecular structures and their reaction routes, our new sampling method that measures the path-informed structural similarity allows us to effectively generate structurally analogous synthesizable molecules. When targeting out-of-domain as well as in-domain seed structures, our model generates structurally and property-wisely similar molecules equipped with well-defined reaction paths. By focusing on the important region in chemical space, we also demonstrate that our model can design new molecules with even higher activity than the seed molecules. | - |
dc.language | English | - |
dc.publisher | ML Research Press | - |
dc.title | Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules | - |
dc.type | Conference | - |
dc.identifier.wosid | 000900064907004 | - |
dc.identifier.scopusid | 2-s2.0-85150643735 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 16952 | - |
dc.citation.endingpage | 16968 | - |
dc.citation.publicationname | 39th International Conference on Machine Learning, ICML 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Baltimore, MD | - |
dc.contributor.localauthor | Jung, Yousung | - |
dc.contributor.nonIdAuthor | Jeong, Dae-Woong | - |
dc.contributor.nonIdAuthor | Kim, Kiyoung | - |
dc.contributor.nonIdAuthor | Han, Se-Hui | - |
dc.contributor.nonIdAuthor | Lee, Moontae | - |
dc.contributor.nonIdAuthor | Lee, Honglak | - |
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