Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease.
Alzheimers Res Ther
; 13(1): 92, 2021 05 03.
Article
in En
| MEDLINE
| ID: mdl-33941241
ABSTRACT
BACKGROUND:
Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective.METHODS:
In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes.RESULTS:
We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib).CONCLUSIONS:
Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Pharmaceutical Preparations
/
Alzheimer Disease
Limits:
Humans
Language:
En
Journal:
Alzheimers Res Ther
Year:
2021
Document type:
Article
Affiliation country:
Japan