MULGA, a unified multi-view graph autoencoder-based approach for identifying drug–protein interaction and drug repositioning
Abstract Motivation Identifying drug–protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety...
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| Published in: | Bioinformatics (Oxford, England) Vol. 39; no. 9 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford University Press
02.09.2023
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| ISSN: | 1367-4811, 1367-4803, 1367-4811 |
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| Abstract | Abstract
Motivation
Identifying drug–protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed.
Results
To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new “guilty-by-association”-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning.
Availability and implementation
MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/. |
|---|---|
| AbstractList | Abstract
Motivation
Identifying drug–protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed.
Results
To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new “guilty-by-association”-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning.
Availability and implementation
MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/. Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed.MOTIVATIONIdentifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed.To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning.RESULTSTo address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning.MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/.AVAILABILITY AND IMPLEMENTATIONMULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/. |
| Author | Li, Chen Wang, Zhikang Gao, Xin Song, Jiangning Zhang, Yiwen Liu, Hui Zhang, Lin Ma, Jiani Li, Shanshan Guo, Yuming |
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| Cites_doi | 10.1093/nar/gkab122 10.1093/bib/bbac054 10.1371/journal.pcbi.1007129 10.1038/s41401-020-0485-4 10.1371/journal.pone.0249404 10.1016/j.patrec.2005.10.010 10.1021/ci00046a002 10.1093/nar/gkl999 10.1093/bioinformatics/btab715 10.1016/S0140-6736(20)30183-5 10.1093/bioinformatics/btaa921 10.1021/c160017a018 10.1093/nar/gkx1037 10.1021/ci400709d 10.1371/journal.pone.0118432 10.1002/prot.20045 10.1093/bioinformatics/bty593 10.1093/bioinformatics/bth466 10.1093/nar/gky1049 10.1023/A:1007091128394 10.1021/acs.jcim.9b00408 10.1021/ci010132r 10.1056/NEJMoa2001017 10.1093/bib/bbz041 10.1093/bioinformatics/btaa544 10.1089/cmb.2020.0538 10.1038/nbt.1990 10.1016/j.patcog.2017.08.024 10.1038/s42256-020-0152-y 10.1038/s41589-022-01131-2 10.1093/bib/bbac272 10.1016/j.coph.2010.09.016 10.1371/journal.pcbi.1004760 10.1093/bib/bbac269 10.1038/nrd.2018.168 |
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| Snippet | Abstract
Motivation
Identifying drug–protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be... Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating... |
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| SubjectTerms | Original Paper |
| Title | MULGA, a unified multi-view graph autoencoder-based approach for identifying drug–protein interaction and drug repositioning |
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