Human Heredity

Original Paper

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

Prinz J.a · Koohi-Moghadam M.a,b · Sun H.b · Kocher J.-P.A.a · Wang J.a,c

Author affiliations

aDepartment of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA
bDepartment of Chemistry, The University of Hong Kong, Hong Kong, SAR, China
cDepartment of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA

Keywords: Drug-target interactionDrug side effectGenome-wide association studiesNeural networkPrincipal component analysis

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Hum Hered 2017/2018;83:79–91
https://doi.org/10.1159/000492574

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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: April 12, 2018
Accepted: July 30, 2018
Published online: October 22, 2018
Issue release date: November 2018

Number of Print Pages: 13
Number of Figures: 6
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

Abstract

Aims: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. Methods: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. Results: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. Conclusion: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

© 2018 S. Karger AG, Basel



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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: April 12, 2018
Accepted: July 30, 2018
Published online: October 22, 2018
Issue release date: November 2018

Number of Print Pages: 13
Number of Figures: 6
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

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Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
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2017/2018, Vol.83, No. 2

November 2018

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