SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures
Joint Authors
Yan, Cheng
Lan, Wei
Pan, Yi
Wang, Jianxin
Wu, Fang-Xiang
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process.
Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks.
However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases.
In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels.
SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases.
Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively.
This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.
American Psychological Association (APA)
Yan, Cheng& Wang, Jianxin& Lan, Wei& Wu, Fang-Xiang& Pan, Yi. 2017. SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures. Complexity،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142665
Modern Language Association (MLA)
Yan, Cheng…[et al.]. SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures. Complexity No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142665
American Medical Association (AMA)
Yan, Cheng& Wang, Jianxin& Lan, Wei& Wu, Fang-Xiang& Pan, Yi. SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures. Complexity. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142665
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142665