Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

Abstract:

:Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Udrescu L,Sbârcea L,Topîrceanu A,Iovanovici A,Kurunczi L,Bogdan P,Udrescu M

doi

10.1038/srep32745

subject

Has Abstract

pub_date

2016-09-07 00:00:00

pages

32745

issn

2045-2322

pii

srep32745

journal_volume

6

pub_type

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