Drug–Target Interaction Prediction Based on an Interactive Inference Network Article Swipe
Yuqi Chen
,
Xiaomin Liang
,
Wei Du
,
Yanchun Liang
,
Garry Wong
,
Liang Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ijms25147753
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ijms25147753
Drug–target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug–target interactions (DTIs). In this study, a novel drug–target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug–target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug–target interactions. Additionally, we predicted and validated 22 Alzheimer’s disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.
Related Topics
Concepts
DrugBank
Interpretability
Drug repositioning
Computer science
Inference
PubChem
Repurposing
Drug-drug interaction
Artificial intelligence
Interaction network
Drug
Chemical space
Drug target
Drug discovery
Drug development
Machine learning
Identification (biology)
Fingolimod
Interaction information
Computational biology
Bioinformatics
Pharmacology
Chemistry
Biology
Mathematics
Statistics
Botany
Ecology
Immunology
Gene
Multiple sclerosis
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijms25147753
- https://www.mdpi.com/1422-0067/25/14/7753/pdf?version=1721121788
- OA Status
- gold
- Cited By
- 2
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400641105
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400641105Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijms25147753Digital Object Identifier
- Title
-
Drug–Target Interaction Prediction Based on an Interactive Inference NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-15Full publication date if available
- Authors
-
Yuqi Chen, Xiaomin Liang, Wei Du, Yanchun Liang, Garry Wong, Liang ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/ijms25147753Publisher landing page
- PDF URL
-
https://www.mdpi.com/1422-0067/25/14/7753/pdf?version=1721121788Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1422-0067/25/14/7753/pdf?version=1721121788Direct OA link when available
- Concepts
-
DrugBank, Interpretability, Drug repositioning, Computer science, Inference, PubChem, Repurposing, Drug-drug interaction, Artificial intelligence, Interaction network, Drug, Chemical space, Drug target, Drug discovery, Drug development, Machine learning, Identification (biology), Fingolimod, Interaction information, Computational biology, Bioinformatics, Pharmacology, Chemistry, Biology, Mathematics, Statistics, Botany, Ecology, Immunology, Gene, Multiple sclerosis, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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