Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction

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dc.contributor.authorZheng, L.N.
dc.contributor.authorDong, C.G.
dc.contributor.authorZhang, W.E.
dc.contributor.authorChen, X.
dc.contributor.authorYue, L.
dc.contributor.authorChen, W.
dc.contributor.conference33rd ACM International Conference on Information and Knowledge Management (CIKM) (21 Oct 2024 - 25 Oct 2024 : Boise, Idaho, USA)
dc.date.issued2024
dc.description.abstractDrug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk and could be life-critical if overlooked, exemplifying the long-tailed distribution problem. Existing models falter against this distribution challenge and overlook the multi-faceted nature of drugs in DDI prediction. In this paper, a novel multi-modal deep learningbased framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. The proposed framework fuses multimodal features of drugs, including graphbased, molecular structure, Target and Enzyme, for DDI identification. To tackle the challenge posed by the distribution skewness across categories, a novel loss function called Tailed Focal Loss is introduced, aimed at further enhancing the model performance and address gradient vanishing problem of focal loss in extremely long-tailed dataset. Intensive experiments over 4 challenging long-tailed dataset demonstrate that the TFMD outperforms the most recent SOTA methods in long-tailed DDI classification tasks. The source code is released to reproduce our experiment results: https://github.com/IcurasLW/TFMD_Longtailed_DDI.git
dc.description.statementofresponsibilityLiangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang, Xin Chen, Lin Yue, Weitong Chen
dc.identifier.citationProceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), 2024, pp.3395-3404
dc.identifier.doi10.1145/3627673.3679719
dc.identifier.isbn979-8-4007-0436-9
dc.identifier.issn2155-0751
dc.identifier.orcidZheng, L.N. [0009-0007-2793-8110]
dc.identifier.orcidDong, C.G. [0009-0005-1495-6534]
dc.identifier.orcidZhang, W.E. [0000-0002-0406-5974]
dc.identifier.orcidYue, L. [0000-0001-9086-1805] [0000-0003-3007-1347]
dc.identifier.orcidChen, W. [0000-0003-1001-7925]
dc.identifier.urihttps://hdl.handle.net/2440/148018
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.placeNew York, NY, USA
dc.relation.granthttp://purl.org/au-research/grants/arc/LP230200821
dc.relation.granthttp://purl.org/au-research/grants/arc/IE240100275
dc.rights© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
dc.source.urihttps://dl.acm.org/doi/proceedings/10.1145/3627673
dc.subjectData Mining; Drug-Drug Interaction; Medical Machine Learning; Multi-Modal Learning; Long-tailed Learning
dc.titleDevil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction
dc.typeConference paper
pubs.publication-statusPublished

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