Next Article in Journal
Brain Delivery of a Potent Opioid Receptor Agonist, Biphalin during Ischemic Stroke: Role of Organic Anion Transporting Polypeptide (OATP)
Previous Article in Journal
Development and Characterization of the Solvent-Assisted Active Loading Technology (SALT) for Liposomal Loading of Poorly Water-Soluble Compounds
Previous Article in Special Issue
Computational Drug Repurposing Algorithm Targeting TRPA1 Calcium Channel as a Potential Therapeutic Solution for Multiple Sclerosis
Open AccessArticle

A Multi-Label Learning Framework for Drug Repurposing

by Suyu Mei 1,* and Kun Zhang 2,*
1
Software College, Shenyang Normal University, Shenyang 110034, China
2
Bioinformatics Core of Xavier NIH RCMI Cancer Research Center, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
*
Authors to whom correspondence should be addressed.
Pharmaceutics 2019, 11(9), 466; https://doi.org/10.3390/pharmaceutics11090466
Received: 27 July 2019 / Revised: 22 August 2019 / Accepted: 5 September 2019 / Published: 9 September 2019
(This article belongs to the Special Issue Computational Drug Repurposing)
Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l2-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes. View Full-Text
Keywords: drug-target interaction; drug repurposing; drug-disease associations; multi-label learning; stratified multi-label cross validation drug-target interaction; drug repurposing; drug-disease associations; multi-label learning; stratified multi-label cross validation
Show Figures

Figure 1

MDPI and ACS Style

Mei, S.; Zhang, K. A Multi-Label Learning Framework for Drug Repurposing. Pharmaceutics 2019, 11, 466.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop