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Cited 20 time in webofscience Cited 23 time in scopus
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Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development

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dc.contributor.authorDanishuddin-
dc.contributor.authorJamal, Mohammad Sarwar-
dc.contributor.authorSong, Kyoung-Seob-
dc.contributor.authorLee, Keun-Woo-
dc.contributor.authorKim, Jong-Joo-
dc.contributor.authorPark, Yeong-Min-
dc.date.accessioned2024-01-11T02:00:21Z-
dc.date.available2024-01-11T02:00:21Z-
dc.date.issued2023-12-
dc.identifier.issn1424-8247-
dc.identifier.issn1424-8247-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/69189-
dc.description.abstractPROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are “undruggable” or challenging to target through chemical molecules via the Ubiquitin–Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area. © 2023 by the authors.-
dc.language영어-
dc.language.isoENG-
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)-
dc.titleRevolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/ph16121649-
dc.identifier.scopusid2-s2.0-85180681428-
dc.identifier.wosid001131138000001-
dc.identifier.bibliographicCitationPharmaceuticals, v.16, no.12-
dc.citation.titlePharmaceuticals-
dc.citation.volume16-
dc.citation.number12-
dc.type.docTypeReview-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaPharmacology & Pharmacy-
dc.relation.journalWebOfScienceCategoryChemistry, Medicinal-
dc.relation.journalWebOfScienceCategoryPharmacology & Pharmacy-
dc.subject.keywordPlusE3 UBIQUITIN LIGASES-
dc.subject.keywordPlusPROTEIN-DEGRADATION-
dc.subject.keywordPlusPREDICTION-
dc.subject.keywordPlusCHIMERAS-
dc.subject.keywordPlusLIGANDS-
dc.subject.keywordPlusCOMPLEX-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordAuthorartificial intelligence-
dc.subject.keywordAuthorE3 ligases-
dc.subject.keywordAuthorlinker-
dc.subject.keywordAuthorPROTACs-
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