Predicting drug-target interactions based on dynamic multi-grained scanning

ZHANG Qi; YIN Zhixiang; LU Lin

doi:10.12299/jsues.24-0143

Volume 39 Issue 3

Sep. 2025

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Article Navigation > Journal of Shanghai University of Engineering Science > 2025 > 39(3): 354-359

ZHANG Qi, YIN Zhixiang, LU Lin. Predicting drug-target interactions based on dynamic multi-grained scanning[J]. Journal of Shanghai University of Engineering Science, 2025, 39(3): 354-359. doi: 10.12299/jsues.24-0143

Citation:

ZHANG Qi, YIN Zhixiang, LU Lin. Predicting drug-target interactions based on dynamic multi-grained scanning[J]. Journal of Shanghai University of Engineering Science, 2025, 39(3): 354-359. doi: 10.12299/jsues.24-0143

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Predicting drug-target interactions based on dynamic multi-grained scanning

doi: 10.12299/jsues.24-0143

ZHANG Qi^1
,,
YIN Zhixiang^{1
,
,},
LU Lin²

1.
School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Shanghai Xinhao Information Technology Co., Ltd., Shanghai 200335, China

Received Date: 2024-05-28
Available Online: 2025-12-22
Publish Date: 2025-09-30

Abstract

Abstract

To address the poor classification performance of traditional machine learning models in the drug-target prediction, a problem caused by their shallow structure and complex data features, a novel prediction model DMS-DF was proposed. The model was based on the deep forest algorithm, the model incorporated a dynamic adaptive multi-granularity scanning mechanism. Furthermore, CatBoost and XGBoost were selected as cascade forest-based classifiers. It demonstrates that that the DMS-DF model outperforms the other four models in terms of drug-target prediction on the same dataset, providing a novel approach for drug discovery.
- drug-target interactions,
- machine learning,
- multi-grained cascade forest (gcForest) model,
- multi-grained scanning

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References(26)

References

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