CCL-DTI : contributing the contrastive loss in drug-target interaction prediction
© 2024. The Author(s)..
BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.
RESULTS: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets.
CONCLUSIONS: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:25 |
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| Enthalten in: |
BMC bioinformatics - 25(2024), 1 vom: 30. Jan., Seite 48 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dehghan, Alireza [VerfasserIn] |
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| Links: |
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| Themen: |
Contrastive loss function |
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| Anmerkungen: |
Date Completed 01.02.2024 Date Revised 01.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12859-024-05671-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367816814 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module | ||
| 520 | |a RESULTS: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets | ||
| 520 | |a CONCLUSIONS: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Contrastive loss function | |
| 650 | 4 | |a Drug discovery | |
| 650 | 4 | |a Drug–target interaction | |
| 650 | 4 | |a Multimodal deep learning | |
| 700 | 1 | |a Abbasi, Karim |e verfasserin |4 aut | |
| 700 | 1 | |a Razzaghi, Parvin |e verfasserin |4 aut | |
| 700 | 1 | |a Banadkuki, Hossein |e verfasserin |4 aut | |
| 700 | 1 | |a Gharaghani, Sajjad |e verfasserin |4 aut | |
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