Cited 0 times in Scipus Cited Count

Drug Induced Liver Injury Prediction with Injective Molecular Transformer

DC Field Value Language
dc.contributor.authorChoi, G-
dc.contributor.authorCho, HJ-
dc.contributor.authorKim, SS-
dc.contributor.authorHan, JE-
dc.contributor.authorCheong, JY-
dc.contributor.authorHong, C-
dc.description.abstractDrug-Induced Liver Injury (DILI), liver damage caused by drugs, represents a significant factor contributing to the failure of clinical trials. Remarkably, the drug development process, which entails an extensive timeline spanning several years and incurring costs of billions of dollars to achieve Food and Drug Administration (FDA) approval, could greatly benefit from early DILI prediction. Furthermore, through the utilization of DILI prediction, clinicians can obtain valuable insights into the potential risks associated with medication, empowering them to make more informed decisions when prescribing drugs to patients. We employ Graph Neural Networks (GNNs) to predict DILI based on drug structures. GNNs consist of node aggregation, which gathers node representations, and graph pooling, which compiles node representations to portray the graph as a single vector. While the graph pooling method built on Set Transformer outperforms existing techniques, we identify a limitation: Set Transformer uses a random seed vector as the query vector that cannot differentiate between graphs of varied structures. Moreover, it potentially lacks expressiveness, as it is randomly defined without prior knowledge and relies on a limited number of seed vectors. To overcome the issues, we introduce Molecular Transformer that employs unique molecular representations as the query vectors. We find that using drug toxicity information extracted from relevant knowledge-bases as the query vector yields the best performance.-
dc.titleDrug Induced Liver Injury Prediction with Injective Molecular Transformer-
dc.contributor.affiliatedAuthorCho, HJ-
dc.contributor.affiliatedAuthorKim, SS-
dc.contributor.affiliatedAuthorHan, JE-
dc.contributor.affiliatedAuthorCheong, JY-
dc.type.localJournal Papers-
dc.citation.titleBHI 2023 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings-
dc.identifier.bibliographicCitationBHI 2023 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings, : 1-4, 2023-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Gastroenterology
Files in This Item:
There are no files associated with this item.


해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.