Citation

Saranya, K. and Joseph, Emerson Raja and Sonai Muthu Anbananthen, Kalaiarasi and Karthiga, M. (2026) Generative AI in drug repurposing and biomarker discovery: a multimodal approach. Frontiers in Bioinformatics, 6. ISSN 2673-7647

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Abstract

Introduction: Computational drug repurposing has been widely explored using similarity-based methods, network diffusion, matrix factorization, deep learning, and graph neural networks (GNNs). However, recent heterogeneous GNN models, such as TxGNN and GAT-based models, demonstrate serious limitations for real-world biomedical applications, including poor generalization to sparsely annotated diseases, limited disease-level adaptation, and inability to effectively combine heterogeneous evidence from curated databases, multi-omics profiles, and unstructured biomedical literature. Methods: This article proposes a heterogeneous attention-based meta-learning graph neural network named HAMGNN, which employs three major innovations: (i) relation-sensitive multi-head attention to prioritize biologically significant interactions among heterogeneous edge types, (ii) a disease-focused metalearning framework enabling rapid adaptation to newly observed or underinformed diseases, and (iii) a literature-enhanced knowledge graph construction pipeline encoding high-confidence, LLM-extracted therapeutic information. The model was tested on a large multimodal biomedical knowledge graph assembled from DrugBank, DisGeNET, and Hetionet, comprising more than 2.2 million edges, using a stringent disjoint disease-based (cold-start) evaluation protocol. Results: HAMGNN achieved a receiver operating characteristic–area under the curve (ROC–AUC) of 0.98 and precision of 0.95, representing a 10%–15% improvement over TxGNN and GAT-GNN on unseen disease generalization. Translational applicability was demonstrated through Alzheimer’s disease and Long COVID case studies, identifying clinically plausible repurposing candidates and disease-associated biomarker signatures via mechanistic pathways. Discussion: HAMGNN offers a generalized, biologically grounded, and unified framework for evidence-based drug repurposing and biomarker discovery in complex and emerging diseases.

Item Type:	Article
Uncontrolled Keywords:	Generative artificial intelligence, heterogeneous graph neural networks
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	05 May 2026 07:53
Last Modified:	05 May 2026 07:53
URII:	http://shdl.mmu.edu.my/id/eprint/15896

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