DTiGEMS plus : drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques
Open Access
- 29 June 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Journal of Cheminformatics
- Vol. 12 (1), 1-17
- https://doi.org/10.1186/s13321-020-00447-2
Abstract
In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug-target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predictsDrug-Targetinteractions usingGraphEmbedding, graphMining, andSimilarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely: drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.Funding Information
- King Abdullah University of Science and Technology (BAS/1/1606-01-01, BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-26-01, FCC/1/1976-17-01)
This publication has 89 references indexed in Scilit:
- Drug Target Prediction and Repositioning Using an Integrated Network-Based ApproachPLOS ONE, 2013
- Drug target prediction using adverse event report systems: a pharmacogenomic approachBioinformatics, 2012
- Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated frameworkBioinformatics, 2010
- Supervised prediction of drug–target interactions using bipartite local modelsBioinformatics, 2009
- ChemmineR: a compound mining framework for RBioinformatics, 2008
- Prediction of drug–target interaction networks from the integration of chemical and genomic spacesBioinformatics, 2008
- DrugBank: a knowledgebase for drugs, drug actions and drug targetsNucleic Acids Research, 2007
- SuperTarget and Matador: resources for exploring drug-target relationshipsNucleic Acids Research, 2007
- Drug repositioning: identifying and developing new uses for existing drugsNature Reviews Drug Discovery, 2004
- The price of innovation: new estimates of drug development costsJournal of Health Economics, 2003