Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/33590
DC FieldValueLanguage
dc.contributor.authorGeorgiev, Dimitaren_US
dc.contributor.authorToshevska, Martinaen_US
dc.contributor.authorGievska, Sonjaen_US
dc.date.accessioned2025-05-21T08:03:36Z-
dc.date.available2025-05-21T08:03:36Z-
dc.date.issued2024-05-20-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/33590-
dc.description.abstractGraph neural networks (GNN), primed to extract knowledge and discover patterns in graph-structured data, have received particularly increased attention in biomedical research. By integrating information from a variety of biomedical knowledge repositories they offer a fast and efficient computational alternative approach to the costly and time-consuming process of drug development and research. The core contributions of this paper include the design and empirical evaluation of several GNN-based models for the identification of potential HIV (Human Immunodeficiency Virus) inhibitors. In particular, the predictive power of model variants based on Graph Attention Network (GAT), Graph Isomorphism Network (GIN), and Continuous Kernel-Based Graph Convolutional Network, specifically developed to handle molecular data, have been investigated. To assess the effectiveness of the proposed models, the Stanford open graph benchmark dataset for molecular data ogbg-molhiv was used. Furthermore, two types of molecular fingerprints have been proposed to augment the molecular representation in the proposed graph neural models, leading to better performance standing compared to the original models. The paper provides a detailed description of the proposed models for identifying HIV inhibitors, followed by a comparative analysis of the experimental results focusing on a discussion of the challenges we face and future research directions that could be investigated.en_US
dc.publisherIEEEen_US
dc.subjectGraph neural networks , graph convolutional layers , HIV Inhibitors , molecular fingerprints , molecular representationsen_US
dc.titleIdentification of HIV Inhibitors Using Graph Neural Networksen_US
dc.typeProceedingsen_US
dc.relation.conference2024 47th MIPRO ICT and Electronics Convention (MIPRO)en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
Show simple item record

Google ScholarTM

Check


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