Evidence map›Paper›PMID 42576503›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2026

[Prediction and verification of therapeutic drugs for triple-negative breast cancer using a knowledge graph-based drug repurposing model].

Diheng Wu, Zhanfa Xu, Yi Li, Mingxu Zhang, Jiaze Lin, Yijun Lü, Daogang Guan, Genggeng Qin

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Diheng WuSchool of Biomedical Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Zhanfa XuSchool of Biomedical Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Yi LiDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Mingxu ZhangSchool of Biomedical Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Jiaze LinSchool of Biomedical Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Yijun LüDepartment of Imaging Diagnosis, Nanfang Hospital, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Daogang GuanDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Genggeng QinSchool of Biomedical Engineering, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.

Funding

National Natural Science Foundation of China 3257071033
6 · The paper itself

Abstract

objectivesTo construct a knowledge graph-based drug repurposing model for predicting potential therapeutic drugs for triple-negative breast cancer (TNBC).

methodsDrug-target interaction (DTI) affinity data were collected from the BindingDB database and filtered (including data of Kd, Ki, EC

resultsIn the DTI affinity prediction task, KGNN outperformed the benchmark models including KronRLS, SimBoost, DeepDTA, FusionDTA, and GraphDTA (MSE=3.2697, PCC=0.8037, and CI=0.7862). Ablation studies confirmed the critical roles of the modules for enhancing model performance (multi-head attention increased MSE by 5.60%; PPI fusion increased MSE by 10.82%). In cold-start scenarios, KGNN maintained superior performance over the comparators in unseen drug/target settings, demonstrating robust generalization. The TNBC candidate drug predictions well aligned with docking affinities and dynamics simulations (Pearson correlation coefficient>0.85), while attention visualization highlighted the efficacy hotspots.

conclusionsThe KGNN model can effectively predict drug-target interactions to facilitate drug repurposing and the design of multi-target drugs while reducing the screening space and experimental validation costs.

Indexed as

Antineoplastic AgentsDrug RepositioningTriple Negative Breast NeoplasmsComputational BiologyFemaleGraph Neural NetworksHumansAntineoplastic Agentsattention mechanismdrug repurposingdrug-target binding affinityknowledge-aware graph neural networkknowledge graphtriple-negative breast cancer

Identifiers

PMID42576503
PMCPMC13458599

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.