Generative Adversarial Networks for De Novo Molecular Design
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Young Jae | - |
dc.contributor.author | Kahng, Hyungu | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2022-03-15T08:42:13Z | - |
dc.date.available | 2022-03-15T08:42:13Z | - |
dc.date.created | 2022-03-14 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1868-1743 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/139049 | - |
dc.description.abstract | In the chemical industry, the generation of novel molecular structures with beneficial pharmacological and physicochemical properties in de novo molecular design is a critical problem. The advent of deep learning and neural generative models has recently enabled significant achievements in constructing molecular design models in de novo design. Consequently, studies on new generative models continue to generate molecules that exhibit more useful chemical properties. In this study, we propose a method for de novo design that utilizes generative adversarial networks based on reinforcement learning for realistic molecule generation. This method learns to reproduce the training data distribution of simplified molecular-input line-entry system strings. The proposed method is demonstrated to effectively generate novel molecular structures from five benchmark results using a real-world public dataset, ChEMBL. The code is available at https://github.com/ dudwojae/SMILES-MaskGAN. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY-V C H VERLAG GMBH | - |
dc.subject | DRUG DISCOVERY | - |
dc.title | Generative Adversarial Networks for De Novo Molecular Design | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1002/minf.202100045 | - |
dc.identifier.scopusid | 2-s2.0-85109149705 | - |
dc.identifier.wosid | 000756988800002 | - |
dc.identifier.bibliographicCitation | MOLECULAR INFORMATICS, v.40, no.10 | - |
dc.relation.isPartOf | MOLECULAR INFORMATICS | - |
dc.citation.title | MOLECULAR INFORMATICS | - |
dc.citation.volume | 40 | - |
dc.citation.number | 10 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Pharmacology & Pharmacy | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Medicinal | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | DRUG DISCOVERY | - |
dc.subject.keywordAuthor | de novo molecular design | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | generative adversarial networks | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | simplified molecularinput line-entry system (SMILES) strings | - |
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