DSPE: An End-to-End Drug Synergy Combination Prediction Algorithm for Echinococcosis

Haitao Li; Liyuan Jiang; Yuanyuan Chu; Yuansheng Liu; Chunhou Zheng; Yansen Su

doi:10.2174/0115748936325300241009051347

ISSN: 1574-8936
E-ISSN: 2212-392X

DSPE: An End-to-End Drug Synergy Combination Prediction Algorithm for Echinococcosis
Authors: Haitao Li^1,2, Liyuan Jiang^1,2, Yuanyuan Chu^1,2, Yuansheng Liu³, Chunhou Zheng^1,2 and Yansen Su^1,2
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¹ Key Laboratory of Intelligent Computing and Signal Processing, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601 Hefei, China ; ² Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei, 230088, Anhui, China ; ³ College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
Source: Current Bioinformatics, Volume 21, Issue 1, Jan 2026, p. 51 - 62
DOI: https://doi.org/10.2174/0115748936325300241009051347
- Received: 21 May 2024
- Accepted: 05 Sep 2024
- Available online: 07 Jan 2025

Abstract

Background

Echinococcosis, a parasitic disease caused by the larvae of the Echinococcus parasite, poses a serious threat to human health. Medication is an indispensable means of treatment for Echinococcosis; however, due to the less satisfactory efficacy of single drugs, identifying effective drug combinations for the treatment of Echinococcosis is essential. Yet, current predictive models for drug synergy in Echinococcosis face accuracy challenges due to data scarcity, method limitations, and insufficient feature representation.

Objective

This work aims to design an end-to-end method to predict drug synergistic combinations, which enables efficient and accurate identification of drug combinations against Echinococcosis.

Methods

In this work, an end-to-end method, named DSPE, is proposed for predicting anti-Echinococcosis drug synergistic combinations. In DSPE, a dataset of Echinococcosis drug synergistic combinations is constructed by retrieving and extracting information from related scientific articles. Further, DSPE employs a residual graph attention network to deeply analyze drug characteristics and their interactions, thereby enhancing the performance of deep learning models. It also explores the protein-protein interaction network related to Echinococcosis, using node2vec combined with an attention mechanism to efficiently encode disease features. Finally, it predicts the synergy of drug combinations based on the Bliss score by integrating drug combinations and disease features.

Results

Experimental evidence shows that DSPE outperforms five state-of-the-art algorithms in predicting drug combination effects by leveraging disease-target information and single-agents for the treatment.

Conclusion

DSPE improves prediction accuracy and addresses the issue of data scarcity for new diseases, offering new insights and methods for the development of treatment plans for parasitic diseases in the future.

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DSPE: An End-to-End Drug Synergy Combination Prediction Algorithm for Echinococcosis

Curr Bioinform 21, 51 (2026); https://doi.org/10.2174/0115748936325300241009051347

/content/journals/cbio/10.2174/0115748936325300241009051347

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Supplements

Supplementary material is available on the publisher’s website along with the published article.

Article Type: Research Article

Keyword(s): disease-target information; drug combination; drug synergistic combination prediction; Echinococcosis; residual graph attention network; single drug

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DSPE: An End-to-End Drug Synergy Combination Prediction Algorithm for Echinococcosis

Abstract

Supplementary material is available on the publisher’s website along with the published article.

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