MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction
Predicting drug–target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug–target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a mu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/2/274 |
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| author | Jihong Wang Ruijia He Xiaodan Wang Hongjian Li Yulei Lu |
| author_facet | Jihong Wang Ruijia He Xiaodan Wang Hongjian Li Yulei Lu |
| author_sort | Jihong Wang |
| collection | DOAJ |
| description | Predicting drug–target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug–target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. For the target side, it combines MSCNN with Transformer modules to capture both local and global features effectively. The extracted features are then weighted and fused, enabling comprehensive feature representation to enhance the predictive power of the model. Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug–target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. These findings contribute valuable insights for subsequent drug repurposing efforts and novel drug development. |
| format | Article |
| id | doaj-art-5fbd0dfb567e495f877804f4b401117c |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-5fbd0dfb567e495f877804f4b401117c2025-01-24T13:43:22ZengMDPI AGMolecules1420-30492025-01-0130227410.3390/molecules30020274MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction PredictionJihong Wang0Ruijia He1Xiaodan Wang2Hongjian Li3Yulei Lu4School of Computer, Guangdong University of Education, Guangzhou 510310, ChinaSchool of Computer, Guangdong University of Education, Guangzhou 510310, ChinaSchool of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan 528458, ChinaSchool of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan 528458, ChinaSchool of Computer, Guangdong University of Education, Guangzhou 510310, ChinaPredicting drug–target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug–target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. For the target side, it combines MSCNN with Transformer modules to capture both local and global features effectively. The extracted features are then weighted and fused, enabling comprehensive feature representation to enhance the predictive power of the model. Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug–target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. These findings contribute valuable insights for subsequent drug repurposing efforts and novel drug development.https://www.mdpi.com/1420-3049/30/2/274drug-target interactionsMSCNNTransformerBFIMSFM |
| spellingShingle | Jihong Wang Ruijia He Xiaodan Wang Hongjian Li Yulei Lu MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction Molecules drug-target interactions MSCNN Transformer BFIM SFM |
| title | MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction |
| title_full | MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction |
| title_fullStr | MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction |
| title_full_unstemmed | MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction |
| title_short | MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction |
| title_sort | mcf dti multi scale convolutional local global feature fusion for drug target interaction prediction |
| topic | drug-target interactions MSCNN Transformer BFIM SFM |
| url | https://www.mdpi.com/1420-3049/30/2/274 |
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