Computational approaches for predicting drug-disease associations: a comprehensive review

doi:10.1007/s11704-024-40072-y

Front. Comput. Sci.

2025, Vol. 19

Issue (5) : 195909 https://doi.org/10.1007/s11704-024-40072-y

Interdisciplinary

Computational approaches for predicting drug-disease associations: a comprehensive review

Zhaoyang HUANG¹, Zhichao XIAO¹, Chunyan AO^1,², Lixin GUAN¹, Liang YU¹(

)

¹. School of Computer Science and Technology, Xidian University, Xi’an 710071, China
². Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China,Chengdu 611731, China

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Abstract

In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been proposed for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNA-disease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrix-based algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we discuss the current challenges and future perspectives in the field of drug-disease associations.

Keywords drug-disease association association prediction drug repositioning machine learning

Corresponding Author(s): Liang YU

Just Accepted Date: 23 July 2024 Issue Date: 23 September 2024

Cite this article:

Zhaoyang HUANG,Zhichao XIAO,Chunyan AO, et al. Computational approaches for predicting drug-disease associations: a comprehensive review[J]. Front. Comput. Sci., 2025, 19(5): 195909.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-40072-y
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I5/195909

Method	Strategy	Input	Prediction out	Advantage	Disadvantage	Code
GIPAE	FC layerRandom forest	FingerprintDrug Gaussian similarityDisease Gaussian similarityDisease semantic similarity	D-D interactions	Low running timesdoes not need 3D structures	Complex feature representation	See github.com/HanJingJiang/GIPAE website.
SKCNN	CNNRandom forest	Drug sigmoid kernel similarityDrug structure similarityDisease semantic similarityDisease sigmoid kernel similarity	D-D interactions	High accuracydoes not need 3D structures	Complex feature representation	See github.com/HanJingJiang/SKCNN website.
SAeRof	Sparse autoencoderRandom forest	Drug structure similarityDrug Gaussian similarityDisease Gaussian similarityDisease semantic similarity	D-D interactions	Does not need 3D structures	Low accuracycomplex feature representation	See github.com/HanJingJiang/SAEROF website.
GFPred	Graph convolutional autoencoderFC autoencoderAttention mechanism	Drug attributesDrug similarityDisease similarityD-D association	D-D interactions	Does not need 3D structures	Low speed	N
DRRS	SVT algorithm	Drug similarityDisease similarityD-D association	D-D interactions	Low running timesdoes not need 3D structures	Input sparsity affects performanceclassification only	See bioinformatics.csu.edu.cn/resources/softs/DrugRepositioning/DRRS/index.html website.
DNL_2,1-CMF	Dual-network L_2,1-collaborative matrix factorization	Drug similarityDisease similarity	D-D interactions	Low running timesdoes not need 3D structures	Classification only	See github.com/cuizhensdws/drug-disease-datasets website.
CMFMTL	Multi-Task LearningCollective Matrix Factorization	D-D association	D-D interactions	Does not need 3D structures	Classification only	See github.com/LoseHair/CMFMTL website.
DRCFFS	Collaborative filtering	drug chemical structuresdrug target proteinsD-D association	D-D interactions	Low running timesHigh accuracy	Classification only	N
MeSHDD	Bit-wise distancerobust clusters	MEDLINE repository terms	D-D pairs	Text input only	No gold standard testing	See apps.chiragjpgroup.org/MeSHDD/ website.
deepDR	Random walkAutoencoder	10 types of heterogeneous networks	D-D interactions	High accuracy	Low speed	See github.com/ChengF-Lab/deepDR website.
GCN-MF	GCNmatrix factorization	D-G associationGene featuresDisease features	D-G interactions	High accuracy	Low speed	N
OWL	Semantic Web technology	PharmGKBFDA approved BCDs	D-D pairs	Text input only	No gold standard testing	N
SLAP	Semantic Link Association	Semantic linked data	D-D interactions	High accuracy	Classification only	N

Tab.1 Summary of the drug- disease interaction algorithm

Fig.1 The general procedure of predicting drug-disease associations based on neural network algorithms

Fig.2 The general process of drug-disease association prediction based on matrix reconstruction algorithm

Fig.3 The general process of drug-disease association prediction based on matrix factorization algorithm

Fig.4 General process of anticipating the link between drugs and illnesses using recommendation algorithm

Tab.2 Cdatasets dataset

Fig.5 Comparison of different algorithms based on AUC (a) and AUPR (b) values

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