AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction

Cross-validation results across multiple datasets indicate that AMFGNN outperforms seven advanced drug-disease association prediction methods. Additionally, case studies on Hepatoblastoma, asthma and Alzheimer's disease further confirm the model's effectiveness and potential value in real-world applications.

Authors

Guangnan Xing (ORCID: https://orcid.org/0000-0002-2507-9195)
Xiaojing Chang
Mengxian Yu (ORCID: https://orcid.org/0009-0008-2743-0912)
Lian Duan (ORCID: https://orcid.org/0000-0001-8561-5212)
He Fang (ORCID: https://orcid.org/0000-0001-9036-8092)
Huixia Zhou

Institutions

Chinese PLA General Hospital (CN)

Publication Details

Journal: Frontiers in Pharmacology
Published: 2025-04-28
DOI: https://doi.org/10.3389/fphar.2025.1543966
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 0.00

Funders

General Hospital of People’s Liberation Army

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article

AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction

Guangnan Xing, Xiaojing Chang, Mengxian Yu, Lian Duan et al.

Apr 28, 2025

Frontiers in Pharmacology

Computational Drug Discovery Methods

article

AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction

Guangnan Xing, Xiaojing Chang, Mengxian Yu, Lian Duan, He Fang, Huixia Zhou

April 28, 2025article en

Abstract

Cross-validation results across multiple datasets indicate that AMFGNN outperforms seven advanced drug-disease association prediction methods. Additionally, case studies on Hepatoblastoma, asthma and Alzheimer's disease further confirm the model's effectiveness and potential value in real-world applications.

Frontiers in PharmacologyVol. 16

Chinese PLA General Hospital (CN)

General Hospital of People’s Liberation Army

Openalex Percentile: Top 90%

Computational Drug Discovery Methods

DOI Open Access PDF OpenAlex

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