Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLuo, W-
dc.creatorChan, KCC-
dc.publisherBioMed Centralen_US
dc.rights© 2012 Luo and Chan; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe electronic version of this article is the complete one and can be found online at:
dc.titleDiscovering patterns in drug-protein interactions based on their fingerprintsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.issuesuppl. 9-
dcterms.abstractBackground: The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called Drug-Protein Interaction Analysis (D-PIA) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.-
dcterms.abstractMethod: Given a database of drug-protein interactions, D-PIA performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.-
dcterms.abstractResults: To evaluate the effectiveness of D-PIA, we have tested it with real drug-protein interaction data. D-PIA has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if D-PIA can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.-
dcterms.abstractConclusions: D-PIA has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and D-PIA is therefore very fast to compute. D-PIA has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationBMC Bioinformatics, 11 June 2012, v. 13, suppl. 9, S4, p. 1-7-
dcterms.isPartOfBMC bioinformatics-
dc.description.ros2011-2012 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Luo_Drug-protein_Interactions_Fingerprints.pdf633.97 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

Last Week
Last month
Citations as of Sep 24, 2023


Citations as of Sep 24, 2023


Last Week
Last month
Citations as of Sep 21, 2023


Last Week
Last month
Citations as of Sep 21, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.