PPII-AEAT : Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training
Copyright © 2024 Elsevier Ltd. All rights reserved..
Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:172 |
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| Enthalten in: |
Computers in biology and medicine - 172(2024) vom: 19. März, Seite 108287 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Zitong [VerfasserIn] |
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| Links: |
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| Themen: |
Adversarial training |
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| Anmerkungen: |
Date Completed 26.03.2024 Date Revised 26.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2024.108287 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adversarial training | |
| 650 | 4 | |a Autoencoder | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Drug discovery | |
| 650 | 4 | |a Protein-protein interaction inhibitor | |
| 700 | 1 | |a Zhao, Lingling |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Mengyao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yuanlong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Junjie |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chunyu |e verfasserin |4 aut | |
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